Introduction
For decades now, researchers have focused on the characteristics of the language environment and how it shapes language development and knowledge (Hart & Risley, Reference Hart and Risley1995; Hirsh-Pasek et al., Reference Hirsh-Pasek, Adamson, Bakeman, Owen, Golinkoff, Pace, Yust and Suma2015; Hoff & Naigles, Reference Hoff and Naigles2002; Romeo et al., Reference Romeo, Leonard, Robinson, West, Mackey, Rowe and Gabrieli2018). As reported in several studies, both the quantity and the quality of maternal linguistic input determine language learning’s outcomes (Baldwin, Reference Baldwin2000; Hoareau et al., Reference Hoareau, Yeung and Nazzi2019; Hoff & Naigles, Reference Hoff and Naigles2002; Hoff, Reference Hoff2013; Huttenlocher et al., Reference Huttenlocher, Waterfall, Vasilyeva, Vevea and Hedges2010; Weisleder & Fernald, Reference Weisleder and Fernald2013; Weizman & Snow, Reference Weizman and Snow2001). Moreover, the characteristics of the adult’s input are related to language outcomes not only in the home environment but also in educational contexts (Duncan et al., Reference Duncan, King, Finders, Elicker, Schmitt and Purpura2020; Majorano et al., Reference Majorano, Cigala and Corsano2009).
Many classical studies in the 1980s and 1990s reported quantitative and qualitative descriptions of children’s speech production using direct observations (via video- and/or audio-recording) rather than diaries used in previous research (Ferguson et al., Reference Ferguson, Menn and Stoel-Gammon1992; Oller et al., Reference Oller, Eilers, Neal and Schwartz1999; Vihman, Reference Vihman, Mattingly and Studdert-Kennedy1991, Reference Vihman1993). Furthermore, phonetic, phonological and lexical descriptions of the linguistic input have been provided for children with typical language development, children with language delays and children with exposure to several languages (Keren-Portnoy et al., Reference Keren-Portnoy, Majorano and Vihman2009; McGillion et al., Reference McGillion, Herbert, Pine, Vihman, dePaolis, Keren‐Portnoy and Matthews2017; VanDam et al., Reference VanDam, Ambrose and Moeller2012). Observational studies have also described quantitative and qualitative characteristics of Infant Directed Speech (IDS) and Child Directed Speech (CDS) focusing on mother-child interactions (e.g., Soderstrom et al., Reference Soderstrom, Blossom, Foygel and Morgan2008). However, direct observational studies conducted in naturalistic context (e.g., home) have limitations. Firstly, direct observation (which, by nature, has limited duration) cannot estimate the level of language exposure that a child receives during an entire day or for a period of time longer than the observation; secondly, audio-recordings require a lot of work for language transcriptions and analyses. Furthermore, reliability assessments across trained transcribers are a critical element and require additional work. Despite these drawbacks, direct observations of speech production are extremely useful to extrapolate measures of preverbal productions (Majorano et al., Reference Majorano, Guidotti, Guerzoni, Murri, Morelli, Cuda and Lavelli2018, Reference Majorano, Brondino, Morelli, Ferrari, Lavelli, Guerzoni, Cuda and Persici2020). Moreover, they can also be integrated with standardised parent-report measures, such as the PRISE questionnaire (i.e., parent report measure of prelexical vocalizations, Kishon-Rabin et al., Reference Kishon-Rabin, Taitelbaum-Swead, Ezrati-Vinacour and Hildesheimer2005; Italian version by Cuda et al., Reference Cuda, Guerzoni, Mariani, Murri, Biasucci and Fabrizi2013) or the Infant Behavior Questionnaire – especially the Vocal Reactivity scale (first version, Rothbart, Reference Rothbart1981; Italian version by Montirosso et al., Reference Montirosso, Cozzi, Putnam, Gartstein and Borgatti2011). The use of these tools provides an immediate indirect measure of expressive skills in children in the first two years of life.
Researchers have also recently developed automatic systems for speech and language transcriptions and analysis. One of the most important achievement in this domain is the Language ENvironment Analysis system (LENA, LENA Foundation, Boulder, CO, Greenwood et al., Reference Greenwood, Thiemann-Bourque, Walker, Buzhardt and Gilkerson2011). The LENA system has been used in studies spanning across several languages and countries, in basic as well as in applied research (e.g., intervention programmes), and in both clinical and educational settings (for a recent review, see Greenwood et al., Reference Greenwood, Schnitz, Irvin, Tsai and Carta2018). LENA recording system is made up of a hardware and a software component. The hardware includes a digital language processor (DLP) that is hidden in a chest pocket, on a special vest, and records the environmental acoustic input around the wearer (the infant) within a six-foot radius. The LENA software, in turn, provides automated measures of the speech heard and produced by children and adults around them. After analysing the audio-recordings, it generates quantitative assessment of a range of linguistic elements recorded (see below) and arranges this information into visual reports, thus allowing data analysis in an easy-to-read interface.
The LENA device provides several pieces of information about the linguistic and auditory characteristics of the environment. In detail, LENA can be used to automatically estimate: 1) basic meaningful speech (clear speech, recorded near the device) and distant speech (distant and not clear speech); 2) basic non-speech sounds: noise (i.e., all noises that are recognized as not coming from a human vocal tract or from an electronic speaker), television/electronic sounds (i.e., sounds from a television, radio, or other electronic media), and silence; 3) linguistic measures: number of words uttered near the child, presumably by adult caregivers (Adult Word Count, AWC); number of vocalisations produced by the child (Child Vocalization Count, CVC); number of conversational turns (Conversational Turn Count, CTC) between a given child and an adult; Automatic Vocalisation Assessment (AVA) (i.e., a measure of expressive language skills tallied by LENA by comparing the phonemic complexity of the child’s output against an adult American English model). The AVA is not as commonly used as the other measures both for English and non-English studies. In addition, for all these measures, LENA can provide 12-hours statistical projections for recordings with at least 10 hours of recording data (i.e., Projected values).
LENA could be a useful research tool, as it allows automatic calculation of input and production language measures on large time windows. However, before using it for research purposes, one would need, first, to establish that the given automatic measures are accurate (that is, reliable), by comparing automatic outputs with hand-coded measures; second, that LENA measures have concurrent validity, by comparing the LENA output with other assessments, such as standardized parental questionnaires. Regarding reliability (or accuracy), LENA has been validated for several languages, through the systematic comparison between the device’s automated coding and human transcriptions (Bulgarelli & Bergelson, Reference Bulgarelli and Bergelson2019; Christakis et al., Reference Christakis, Gilkerson, Richards, Zimmerman, Garrison, Xu, Gray and Yapanel2009; Cristia et al., Reference Cristia, Lavechin, Scaff, Soderstrom, Rowland, Räsänen, Bunce and Bergelson2021; Richards et al., Reference Richards, Gilkerson, Xu and Topping2017). In particular, validation data have been published for: American English (Xu et al., Reference Xu, Yapanel and Gray2009), European French (Canault et al., Reference Canault, Le Normand, Foudil, Loundon and Thai-Van2016), Dutch (Bruyneel et al., Reference Bruyneel, Demurie, Boterberg, Warreyn and Roeyers2020; Busch et al., Reference Busch, Sangen, Vanpoucke and van Wieringen2018), Vietnamese (Ganek & Eriks-Brophy, Reference Ganek and Eriks-Brophy2018) Chinese (Gilkerson et al., Reference Gilkerson, Zhang, Xu, Richards, Xu, Jiang, Harnsberger and Topping2015), Korean (McDonald et al., Reference McDonald, Kwon, Kim, Lee and Ko2021), Swedish (Schwarz et al., Reference Schwarz, Botros, Marcusson, Tidelius and Marklund2017), Hebrew and Arabic (Levin-Asher et al., Reference Levin-Asher, Segal and Kishon-Rabin2022), and on children growing up in a bilingual French–English environment (Orena et al., Reference Orena, Byers-Heinlein and Polka2019). In addition, accuracy measures have also been provided for Spanish (Weisleder & Fernald, Reference Weisleder and Fernald2013). However, that latter study cannot be considered an official validation; indeed, “validation-like” data provided by this study came from an investigation on linguistic input and expressive linguistic skills in families with low socio-economic status (SES). In particular, since LENA had not been completely validated for Spanish, these researchers conducted a small-scale validation analysis for the AWC, based on 60-minute samples taken from 10 recordings. Results showed high correlation between word counts from human transcribers and the automatic LENA estimates (AWC).
This small piece of evidence is relevant to the usage of LENA with Italian participants, given the similarity between the two languages. However, the conclusions that can be drawn are clearly very limited. Thus, LENA cannot yet be used with reliability to study the Italian population. Moreover, and independently from the language specifically targeted, most of the previously conducted studies have validated the AWC and CVC speech measures, while CTC measures were only validated in Dutch, Chinese, Korean, and Vietnamese (Busch et al., Reference Busch, Sangen, Vanpoucke and van Wieringen2018; Ganek & Eriks-Brophy, Reference Ganek and Eriks-Brophy2018; Gilkerson et al., Reference Gilkerson, Zhang, Xu, Richards, Xu, Jiang, Harnsberger and Topping2015; Pae et al., Reference Pae, Yoon, Seol, Gilkerson, Richards, Ma and Topping2016). Importantly, a recent literature review based on 33 studies reporting LENA-based accuracy measures (Cristia et al., Reference Cristia, Bulgarelli and Bergelson2020) has revealed that only some studies (25 out of 33) provided validity estimates. Furthermore, most of the studies included in Cristia et al.’s systematic work were found to report only limited information about the methodology used to conduct the validation and the results obtained through the validation process. Using broad definitions of recall (accuracy of the LENA system in detecting an event) and precision (accuracy in defining the event), Cristia et al. (Reference Cristia, Bulgarelli and Bergelson2020) found high accuracy for AWC (13 studies, mean r = .79) and CVC (5 studies, mean r = .77) but lower accuracy for CTC – note, however, that CTC reliability was computed on a small set of available studies, 6 studies, mean r = .36). More problematic results in the LENA vs human estimation of CTC, as compared to the other LENA measures, were also reported using five different corpora (AWC r = .70, CVC r = 65 and CTC r = .36; Cristia et al., Reference Cristia, Lavechin, Scaff, Soderstrom, Rowland, Räsänen, Bunce and Bergelson2021; see also the recent study by Ramírez et al., Reference Ramírez, Hippe and Kuhl2021).
Besides reliability, some attention has been paid to the concurrent validity of LENA measures with other language measurements by comparing the automatic LENA measures with scores from standardised language assessment tools or other direct assessments of language skills. In a recent study validating the LENA technology for Hebrew and Arabic (Levin-Asher et al., Reference Levin-Asher, Segal and Kishon-Rabin2022), LENA’s concurrent validity was tested by comparing its outputs to the PRISE questionnaire, and good concurrent validity was found between the LENA automatic scores (CTC, CVC) and such questionnaire, filled out at the same age of the recording. Finally, a meta-analytic study of 13 papers exploring if LENA measures predict later linguistic outcomes (Wang et al., Reference Wang, Williams, Dilley and Houston2020) showed moderate correlations between both CTC and CVC and standardised language outcomes, as well as a low correlation between AWC and the same language measures.
To the best of our knowledge, no previous study has validated the LENA system for the Italian language. Our aim was thus to fill this gap (Study 1). Additionally, we provide a concurrent validity analysis (Study 2) of the automated CVC and CTC estimates.
The current study
The objective of the present study is twofold.
Study 1 aims to establish the validity of the LENA system among 12 Italian families with children aged 1;0 (at the time of the first meeting) and 2;0 (at the time of the second meeting), by assessing whether or not there are significant relationships between the automatic CVC, AWC and CTC provided by the LENA and those provided by manual transcriptions. Based on the literature, we expected the LENA-counts and the human-counts to be significantly and strongly related for CVC and AWC, while we expected a potentially weaker association for CTC (e.g., Ramírez et al., Reference Ramírez, Hippe and Kuhl2021).
Study 2 investigates the concurrent validity of the automatic LENA measures that evaluate the children’s production abilities (CVC and CTC) by comparing these measures with other direct and indirect measures of language development (the total number of vocal productions including both verbal and preverbal productions from an interaction session that has been video-recorded at the child’s home; PRISE; IBQ) (note that Study 2 was conducted across a wider sample of children than Study 1: 19 children longitudinally assessed between the ages of 0;6 months and 2;0 years). We expected to find a positive relationship between automatic LENA counts and the children’s vocal production, as manually tallied by considering the number of vocal tokens (i.e., the total number of vocal productions including both verbal and preverbal productions) produced in a direct naturalistic observation, and between CVC and CTC and the scores obtained in the PRISE questionnaire and in the vocal reactivity scale of the IBQ. In particular, we expected to find a significant correlation between the LENA estimates of speech (in terms of CVC) and the child’s actual speech video/audio recorded during spontaneous interaction with their mothers and the PRISE scores. Moreover, we also expected to find significant links between the number of conversational turns in which the child is involved during the day, as estimated by LENA, and measures of verbal skills and vocal reactivity. There are good reasons to believe that socio-communicative or pragmatic aspects of language, which are captured by CTC, are linked to the child’s expressive skills (Donnelly & Kidd, Reference Donnelly and Kidd2021; Romeo et al., Reference Romeo, Leonard, Robinson, West, Mackey, Rowe and Gabrieli2018).
Study 1
Methods
Participants
Participants included 12 typically developing children (9 males and 3 females) recorded for 11 hours on average on the same day or on consecutive days, at both 1;0 and 2;0. We chose these two time-points because, respectively, they usually correspond to the beginning of early word production and to the more advanced phase of vocabulary extension. No parents reported developmental delays or problems at the time of their child’s birth. Children’s mean weight at birth was 3181 grams (SD = 511). All infants were born in Italy. Parents’ mean years of education were 16.8 (SD = 2.77) for the mothers and 16.3 (SD = 3.98) for the fathers, broadly corresponding to 1st level degree. At the time of the first data collection (when children were 1;0) mothers were 34;7 on average (SD = 5.8) and fathers were 39;8 (SD = 7.86) on average. The families were involved in the study through local services for infants and joined the study voluntarily.
Instruments
LENA
Home language environment measures were conducted using the LENA system. The participating children wore the LENA device in a specially designed vest with a chest pocket. This vest is designed to optimise the quality of the recorded sounds (it has low friction properties) and (allowing to keep the recorder on the infant’s body) to hear and measure accurately the speech produced by infants and around them. This device was specifically created to assess the child’s environment in a typical day and can be used with children in the first three years of life.
Procedure
Language samples collection
Parents of children were asked to use the LENA device on one or more typical days for at least 10 hours. More specifically, on the day of the first meeting, parents were provided with the LENA, and a plasticised sheet containing the instructions for using it. Parents were asked to switch on the device in the early morning, when the child woke up, and to switch it off after 10 hours had passed, or whenever they needed to have some privacy. If the parents decided to switch off the device before 10 hours had passed, they were asked to switch it on again, until they reached such minimum number of recording hours required. During the day of the recording, parents (or the adult staying with the child) were asked to fill in a form to track the main activities for each recorded hour. In this way, we knew in which moments the adult and the child were carrying out specific interactive activities. Parents were also asked to evaluate how typical the day was for the baby and to tell whether or not the child’s speech production that day was in line with what they usually produced. The device was left to the families for a maximum of five days from the day of the visit. Families were asked to record children in natural and spontaneous situations that reflected their child’s daily life (e.g., child at home with the parents during the weekend or with other caregivers during the week). For privacy reasons, they were explicitly asked not to use the device when their children were at the day-care center. Moreover, parents were asked to avoid using the recorder during special occasions (i.e., a weekend outside with friends).
As described in more depth in the section below, three samples of 10-minute speech were extrapolated for each child at each age point (1;0 and 2;0) for a total of 72 segments (720 minutes, or 12 hours, in total). The adult and child speech were transcribed independently by two native Italian speakers (two young researchers) and analysed by using the CLAN software from CHILDES (MacWhinney, Reference MacWhinney2000).
Segments selection
To select 10-minutes samples, we stuck to the following criteria: the chunks of recordings in which we observed no productions, such as silence due to naptime, were excluded; different types of activities were included (e.g., mealtime, bathtime, storytime, playtime, and time outside) and, following Gilkerson et al. (Reference Gilkerson, Zhang, Xu, Richards, Xu, Jiang, Harnsberger and Topping2015), different moments of the day were selected (morning, 8 am-1 pm; afternoon, 1 pm- 4 pm; late afternoon, 4 pm- 9 pm).
Transcriptions
Transcriptions of the 30-min speech samples per child and per age were done manually by two native Italian speakers using CHAT of CHILDES (Codes for the Human Analysis of Transcripts, MacWhinney, Reference MacWhinney2000). Transcriptions were done regardless of how the speaker was tagged in the LENA system (i.e., for this reason we cannot include a validation of the speakers tags as done by Xu et al., Reference Xu, Yapanel and Gray2009). The second transcriber was enrolled to independently transcribe 33 out of 72 transcripts, to test reliability (see paragraph below). Since the LENA system defines vocalisations by a “breath-group” criterion (Bruyneel et al., Reference Bruyneel, Demurie, Boterberg, Warreyn and Roeyers2020), such that the vocalisation ends each time a 300 ms break occurs, we used ELAN (Version 6.0) [Computer software] (2020) to analyse the exact time in correspondence of the onset of the child’s production. If the child produced reduplicated sounds CVCVCVCV or single segments CV, these counted as one vocalisation; if a pause occurred in a sequence of CV (pause > 300 ms), these counted as two vocalisations. Overlapping speech (both for the adult and child) was excluded from the analysis. Non speech sounds, such as vegetative sounds (e.g., burping, sneezing, and breathing), and fixed signals (e.g., crying and laughing) were not transcribed.
After transcribing the child’s and the adult’s speech, CTC were coded. A conversational turn is a sequence of speech starting from the target child to the adult (occurring within 5 sec) or vice versa. These sequences could be initiated by the child or by the adult and they counted as one CTC if they were in the form “child – adult – child” and two CTC if they were in the form “child – adult – child – adult”. Each CTC was coded in CHAT using the coding string ($CTC). CTC were not counted in case of overlapping speech.
To count the number of AWC (Adult Word Count), CVC (Child Vocalisation Count) and CTC (Conversational Turns Count), the CLAN program was used with the function “freq” for speaker tier (speaker tier in CHAT are assigned with the *) and dependent tier (coding tiers in CHAT are assigned with %).
Human coder reliability
Before assessing the LENA’s reliability, a reliability index of human transcribers was computed, comparing the transcriptions of the two independent transcribers (Transcriber 1 and 2 in Table 1) on a random sample of 45% transcripts (33 out of 72 10-min segments of speech). To do so, we compared the number of vocal tokens produced by the adult (AWC) and by the child (CVC) and the number of conversational turns (CTC) counted by the two coders. Pearson correlations based on these data were very strong for AWC (r = .99, p <.001), CVC (r = .95, p <.001) and CTC (r = .99, p < .001).
Table 1. Counts by two transcribers (and difference) for Adult Words (AWC), Child Vocalisations (CVC) and Conversational Turns (CTC) for each 10-min segment
Data analysis
To assess the reliability (or accuracy) of the LENA system for the Italian language, comparisons between AWC, CVC and CTC estimates (LENA Pro - Graduate Version) and human coders were performed for all 72 selected 10-min chunks. Results were generated using Jamovi (Version 1.2, 2020).
In line with previous validation studies (e.g., Bruyneel et al., Reference Bruyneel, Demurie, Boterberg, Warreyn and Roeyers2020), we conducted t-tests and Pearson correlations between: the LENA-AWC, CVC, and CTC and the human-AWC, CVC, and CTC. Correlations lower than .30 would reflect poor agreement, correlations between .30 and .50 would reflect low agreement, correlations between .50 and .70 would reflect moderate agreement, and correlations higher than .70 would reflect high agreement (Bruyneel et al., Reference Bruyneel, Demurie, Boterberg, Warreyn and Roeyers2020).
Results and Discussion
Each child was recorded for around 11 hours (corresponding to 672 minutes on average, SD = 67.8 minutes) at 1;0 and for 12 hours (corresponding to 746 minutes, SD = 129 minutes) at 2;0. LENA estimates for AWC, CTC and CVC are reported for each child at 1;0 and 2;0 in Table 2.
Table 2. LENA estimates for the entire recording in the group of children at 1;0 and 2;0
Human Estimates versus LENA estimates (on 72 10-min-long segments)
In order to test the validity of LENA estimates, a series of paired samples t-tests and Pearson product-moment zero-order correlations were computed between those estimates and Results are presented in Table 3 for the entire sample, together with the means and standard deviations.
Table 3. Descriptive statistics, and results of the t-tests (and their ps) and correlations coefficients for the human estimates and LENA estimates for AWC, CVC, and CTC
Note. AWC = Adult Word Count; CVC = Child Vocalisation Count; CTC = Conversational Turns Count.
* p < .05.
** p <.001.
*** p < .001.
For AWC, the LENA system slightly overestimated the number of words produced by the adults, if compared to the number of words transcribed by the human transcribers, as reported in Table 3. However, this difference was not statistically significant (p = .289), in line with Cristia et al. (Reference Cristia, Bulgarelli and Bergelson2020), D’Apice et al. (Reference D’Apice, Latham and von Strumm2019) and Gilkerson et al. (Reference Gilkerson, Zhang, Xu, Richards, Xu, Jiang, Harnsberger and Topping2015). Pearson’s correlations indicated that human counts and LENA estimates, in relation to the number of adult words, were significantly, positively and highly correlated (r = .78, p <.001). The group of children was then divided based on the child’s age and correlations were run again for the two ages separately. At both 1;0 and 2;0, correlations between the number of words produced by the adults as reported by the LENA device and as transcribed by the human coder were significant, positive and high (respectively, r = .73, p <.001 at 1;0; r = .83, p <.001 at 2;0). This finding is also in line with other published studies (Busch et al., Reference Busch, Sangen, Vanpoucke and van Wieringen2018; D’Apice et al., Reference D’Apice, Latham and von Strumm2019; Orena et al., Reference Orena, Byers-Heinlein and Polka2019; Pae et al., Reference Pae, Yoon, Seol, Gilkerson, Richards, Ma and Topping2016).
For CVC, the LENA system underestimated the child vocalisations, if compared to the number of vocalisations transcribed by the human transcribers, in line with Canault et al. (Reference Canault, Le Normand, Foudil, Loundon and Thai-Van2016) and Cristia et al. (Reference Cristia, Bulgarelli and Bergelson2020). However, this difference was not statistically significant (p = .345). Pearson’s correlations indicated that human counts and LENA estimates, in relation to the number of children’s vocalisations, were significantly, positively and weakly correlated (r = .47, p <.001). This is slightly weaker than what most previous studies have observed. Thus, we analysed the data again, at each of the two ages separately. At both 1;0 and 2;0, the correlations between the number of vocalisations produced by the child, as reported by the LENA device and as transcribed by the human coder, were significant, positive and moderate (respectively, r = .66, p < .001 at 1;0; r = .51, p = .002 at 2;0). Separate correlation values were closer to the values in the literature (e.g., Cristia et al., Reference Cristia, Bulgarelli and Bergelson2020).
For CTC, in contrast, significant differences emerged between the number of conversational turns found by human counts and by the LENA system (p = .041, in line with Busch et al., Reference Busch, Sangen, Vanpoucke and van Wieringen2018; Cristia et al., Reference Cristia, Bulgarelli and Bergelson2020). Moreover, we found a low correlation between the two measures (r = .33, p = .005). At both 1;0 and 2;0, the correlations between the number of conversational turns as reported by the LENA device and as coded by the human coder were significant, positive and moderate (respectively, r = .43, p = .008 at 1;0; r = .53, p < .001 at 2;0). This weak finding is in line with what other studies have reported and this index needs to be considered with caution when automatically retrieved from LENA system (Cristia et al., Reference Cristia, Bulgarelli and Bergelson2020; Ramírez et al., Reference Ramírez, Hippe and Kuhl2021).
Study 2
The objective of the second study was to assess the LENA’s concurrent validity against other measures of vocal production. The data analysed in this study were part of a wider longitudinal research on mother-child communication, involving children in the first two years of life. We extracted and analysed the automatic measures of fifty-four speech samples collected from longitudinal recording sessions conducted within a group of 19 children. Each recording session lasted around 12 hours (M = 711 minutes, SD = 93.0). In particular, speech samples were selected for analysis if the recording session was longer than 10 hours, and children were in the age range 0;6 months - 2;0 years.
Participants
As described above, the 54 speech samples retained for analysis came from 19 children (12 males, 7 females). As reported in Table 4, fifteen out of 19 children were recorded on several longitudinal appointments (range: 2-5 appointments, i.e., coded as a categorical variable, hereafter called “Time”) between 0;6 and 2;0 years (range: 0;6 months and 11 days – 2;0 years and 27 days; M = 13. 5, SD = 5.86). Four out of 19 children only completed one recording session.
Table 4. Study 2 participants characteristics (Gender and Age (in months) at which we collected the LENA data from each child). For each child, available data were reported
Note. For all children at each time point we considered the automatic measures (CVC and CTC) from at least 10 hours of audio-recording (LENA) and the PRISE questionnaire. For sessions marked(1), we orthographically transcribed the child’s speech produced during the mother-child interactions in the video-recording made at that age; for sessions marked(2), we collected the Vocal Reactivity Scale of the Infant Behaviour Questionnaire (Italian version).
No parents reported developmental delays or problems at the time of their child’s birth. At the time of the first meeting, children’s mean weight at birth was 3158 grams (SD = 451.4). All infants were born in Italy. Parents’ mean years of education were 16.72(SD = 3.31) for the mothers and 15.29 (SD = 4.51) for the fathers, broadly corresponding to 1st level degree. Mothers’ ages were 33;44 on average (SD = 3.88) and fathers’ ages were 37;55 on average (SD = 6.68). The families were involved in the study through local services for infants and joined the study voluntarily.
Procedure
Each family who participated in the study was provided with a LENA device on the day of each home visit (see Procedure section of the Study 1). During this visit, the researcher provided the family with an instruction form to switch the device on/off and obtained informed consent. On the same appointment, the principal caregiver (the mother for all children) and the child were video-recorded in interaction for around 20 minutes. Then, the caregiver was asked to fill two questionnaires regarding the child’s phonological and vocal development (PRISE, IBQ). Each family could keep the LENA device for a maximum of five days from the day of the visit, thus carrying out the recording in this period.
Measures
The LENA Device
An in-depth description of the tool is provided in Study 1. Around 12 hours of recordings from 54 speech samples (M = 711 minutes, SD = 93.0) were considered for the purposes of the present study.
Mother-child naturalistic interaction (video-recording)
Infants were video-recorded for around 20 minutes during spontaneous interaction with their caregiver (i.e., the mother for all participants) while playing with toys provided by the experimenter (duration of the video, M = 20.4, SD = 2.43). In each play session, four sets of toys were provided to the mothers with the aim of stimulating as many spontaneous productions as possible: 1) a food set, 2) a farm set, 3) a transport set, and 4) a nurturing set. Mothers were asked to interact with their children as they usually do, to make the situation as natural and spontaneous as possible. The video-recordings were conducted at the infant’s home, a familiar context suitable for supporting spontaneous production and reducing distractions.
Only child’s speech was transcribed. In particular, children’s number of vocal tokens (i.e., the total number of vocal productions including both verbal and preverbal productions) using CHAT of CHILDES (Codes for the Human Analysis of Transcripts, MacWhinney, Reference MacWhinney2000), and transcriptions were performed using the same criteria as in Study 1 (see Transcriptions paragraph from Study 1). The onset time of each production was annotated on ELAN (Version 6.0) [Computer software] (2020). Crying, vegetative sounds and shouts were not transcribed. Note that, in this second study, we were not able to estimate LENA validity concerning Adult Word Count, since we did not have any concurrent measure of comparison (i.e., no measure of adult speech).
Production of Infant Scale Evaluation (PRISE)
The Italian version of the PRISE questionnaire was provided to parents (Kishon-Rabin L. et al., Reference Kishon-Rabin, Taitelbaum-Swead, Ezrati-Vinacour and Hildesheimer2005, adapted by Cuda et al., Reference Cuda, Guerzoni, Mariani, Murri, Biasucci and Fabrizi2013) during each observation session (see Table 4). PRISE is a parental questionnaire that evaluates a child’s preverbal skills (production of vowels, simple vocalization, babbling and words). The questionnaire is made up of 11 questions and each question can have a score from 0 to 4, based on the percentage of time children show that specific behavior (0 is never, 4 is 100% of the time, always). The maximum score is 44. Cronbach’s alpha is of .87 in the Italian validation (2013) and of .88 in our sample – thus it can be considered very good.
Infant Behaviour Questionnaire (Vocal Reactivity Scale)
The IBQ-R (Italian version by Montirosso et al., Reference Montirosso, Cozzi, Putnam, Gartstein and Borgatti2011) is a parent-based questionnaire that measures 6 domains of the infant’s temperament (activity level, soothability, fear, distress to limitations, smiling and laughter, and duration of orienting). For the present research, we only asked parents to fill the scale related to the child’s ‘Vocal Reactivity’, which refers to the amount of vocalization exhibited by the baby in daily activities (four subscales in the Italian version; Feeding, Bathing and Dressing, Play, Daily Activities). In the Vocal Reactivity scale, parents are asked to rate the frequency of some specific behaviour shown by their child during the last week. The scale is overall made up by 12 items; each of which have to be rated from 1 (never) to 7 (always); when an item is not applicable, it is not considered for the final score. Cronbach’s alpha is of .78 (on average) in the Italian validation (2011).
Data Analysis
To test for concurrent validity, partial Pearson’s correlations controlling for age (as a continuous variable) and time (as categorical variable, in terms of repeated measures, for those children having more than one observation) were run between the automatic LENA measures (CVC and CTC) and the direct and indirect language measures, respectively taken from video-recordings and from the PRISE and IBQ questionnaires. Results were analysed using Jamovi (Version 1.2, 2020).
Results and Discussion
Correlations between LENA estimates and direct language measures (see Table 5)
Children’s vocal tokens retrieved from the transcriptions of the mother-child interactions did significantly, positively correlate with the number of CVC as measured through LENA in a typical day (r = .564, p < .01). However, the number of human-retrieved tokens produced by the children during naturalistic interaction (video-recorded) did not correlate with the CTC as measured by the LENA device (Table 5). These results establish the validity of automatic LENA measurements in describing linguistic skills in terms of tokens children spontaneously produce in daily interactions, regardless of age and the repeated measure effects. The number of tokens expresses a quantitative score that can be strictly linked to the quantity of vocal production as recorded and extrapolated from LENA device (in terms of CVC). Thus, this finding suggests that the LENA device could be an extremely useful tool when the aim is to determine the quantity of speech produced in a typical day. However, we failed to find any concurrent relationship between the human-retrieved tokens produced by the children and the estimate of LENA CTC.
Table 5. Partial correlation table (controlling for the effects of age and time) showing the link between the automatic measure retrieved by the LENA device (CTC, CVC) and direct (tokens) and indirect (Prise, Vocal Reactivity – VR Scale of the IBQ) language measures
Note.
* p < .05.
** p < .01.
*** p < .001.
Correlations between LENA estimates and indirect language measures (see Table 5)
Children’s PRISE scores significantly, positively, but weakly correlated with CVC as measured by the LENA device (r = .279, p < .05). Although this correlation is low, it indicates a tendency for those children scoring higher on the PRISE questionnaire to produce more vocalisation during a typical day, in a spontaneous context. Moreover, we found a significant, positive and low correlation between the CVC and vocal reactivity during play (r = .384, p < .05); and we found a significant, positive and low correlation between the CTC and vocal reactivity during play (r = .422, p < .05) (Table 5). However, no significant relationships were found between the other sub-scales of the Vocal Reactivity Scale and the automatic outputs of the LENA system.
Taken together, these results establish the concurrent validity of LENA with spontaneous measures retrieved in a spontaneous setting and with parent-report tools for providing an estimation of the child’s speech.
General discussion
In the present paper, we report about both reliability (Study 1) and concurrent validity (Study 2) of the LENA tool for a sample of Italian children aged between 0;6 months and 2;0 years. No previous study had investigated such issues in the Italian context.
As for validation of the LENA system, results for the Italian language are in line with most of the validation studies previously conducted for other languages (see Cristia et al., Reference Cristia, Bulgarelli and Bergelson2020 or Cristia et al., Reference Cristia, Lavechin, Scaff, Soderstrom, Rowland, Räsänen, Bunce and Bergelson2021 for some recent reviews of the literature). They establish the reliability of the LENA device for research conducted with Italian speakers.
More specifically, regarding AWC, the degree of correlation found in our study between the LENA outcomes and the human annotations is very high (r = .78), and this holds for both the joint analysis (all ages considered together) and for analyses conducted in single age-groups (1;0 and 2;0). This result is in line with other published studies that have also reported correlation values of .79 on average (for example, r = .89, Busch et al., Reference Busch, Sangen, Vanpoucke and van Wieringen2018; r = .79, D’Apice, Latham, & von Strumm, Reference D’Apice, Latham and von Strumm2019; r = .77, Orena et al., Reference Orena, Byers-Heinlein and Polka2019; r = .72, Pae et al., Reference Pae, Yoon, Seol, Gilkerson, Richards, Ma and Topping2016). Also, in line with previous investigations, we found that LENA slightly, though not significantly, overestimates AWC if compared to human counts (Cristia et al., Reference Cristia, Bulgarelli and Bergelson2020; D’Apice et al., Reference D’Apice, Latham and von Strumm2019; Gilkerson et al., Reference Gilkerson, Zhang, Xu, Richards, Xu, Jiang, Harnsberger and Topping2015).
Regarding CVC, our data are partially in line with what most studies have found. Specifically, we found a low correlation between the LENA and human counts when the analyses were run on all ages pooled together, while other studies found a strong correlation. However, when data were analysed separately based on age subgroups (1;0 and 2;0), the degree of correlation significantly increased, and especially for the group of younger babies, in agreement with Cristia et al. (Reference Cristia, Bulgarelli and Bergelson2020). Additionally, and in line with former reports, we found that LENA slightly, though not significantly, underestimates the number of CVC with respect to human counts (Canault et al., Reference Canault, Le Normand, Foudil, Loundon and Thai-Van2016; Cristia et al., Reference Cristia, Bulgarelli and Bergelson2020).
Regarding CTC, significant differences emerged between the LENA and human estimates, revealing a tendency towards underestimation by the LENA, a finding which is also in line with previous reports (Busch et al., Reference Busch, Sangen, Vanpoucke and van Wieringen2018; Cristia et al., Reference Cristia, Bulgarelli and Bergelson2020). These significant differences were also confirmed by the significant but weak correlations found between the LENA and the human estimates, both in the joint analysis and for analyses conducted in single age-groups (1;0 and 2;0). This second result is also in line with other validation studies which, on average, have found a correlation power of .36 (Cristia et al., Reference Cristia, Bulgarelli and Bergelson2020), where we found a correlation of .327. Indeed, Ramírez et al. (Reference Ramírez, Hippe and Kuhl2021) considered the relation between LENA’s CTC estimates and human CTC estimates in a wider sample of 70 families, with children longitudinally recorded at 0;6, 0;10, 1;2, 1;6, and 2;0. Results showed that LENA CTC and human CTC are not interchangeable measures and that CTC need to be considered with caution when used as an automatically retrieved measure. Moreover, they found that automatic CTC measures were always higher than manual CTC measures. This specific result contrasts with the findings of the present study (lower vs higher estimates), which might be due to differences in the composition of the samples (different ages, or different individual characteristics of adults and children involved) and/or to the characteristics of the Italian vs English language. At any rate, only a few studies have reported validation of the CTC (6 studies out of 33, as to Cristia et al., Reference Cristia, Bulgarelli and Bergelson2020).
The present results show that, in a sample of Italian recordings, LENA was a reliable/accurate tool for the estimation of both AWC and CVC. This is an important point, as AWC can be considered an important index of language input, being strongly correlated to the child’s language outcomes (see Hart & Risley, Reference Hart and Risley1995; Hoff & Naigles, Reference Hoff and Naigles2002; Rowe, Reference Rowe2008, Reference Rowe2012; Hoareau et al., Reference Hoareau, Yeung and Nazzi2019). A potential implication is that AWC automatically calculated by the LENA could be included in assessments of the risk and protective factors for child language development. At the same time, the possibility of automatically assessing a child’s production (in terms of quantity) using the LENA CVC can give researchers an important index of development, especially for children with delay and special needs. In fact, many studies reported that early vocal production is related to language outcomes. For example, lexical production is an important predictor of language and learning outcomes (Baldwin, Reference Baldwin2000; Hoff, Reference Hoff2013; Hoff & Naigles, Reference Hoff and Naigles2002; Huttenlocher et al., Reference Huttenlocher, Waterfall, Vasilyeva, Vevea and Hedges2010; Weizman & Snow, Reference Weizman and Snow2001), while preverbal production predicts early lexical development (Majorano et al., Reference Majorano, Vihman and DePaolis2014; McGillion et al., Reference McGillion, Herbert, Pine, Vihman, dePaolis, Keren‐Portnoy and Matthews2017). However, LENA count of CVC does not distinguish preverbal and verbal production, thus cannot allow to describe in detail the qualitative level of children’ s production. Note that the AVA (Automatic Vocalisation Assessment) index could be considered in such a case, as this index evaluates the child’s vocal maturity in terms of expressive language skills (by comparing the phonemic complexity of the child’s output against an adult American English model). However, since it is not as commonly used as the other measures, it cannot be used for our Italian sample.
To test the concurrent validity of the LENA measures with direct and indirect linguistic outcomes, a second study was run, with a wider sample of Italian children aged between 0;6 and 2;0 years of age. To the best of our knowledge, this is the first LENA validation study using direct measures of linguistic skills (i.e., child’s vocal production counted from direct video-observation) to test the concurrent validity of LENA’s estimations. In addition, in line with other validation studies, we compared parent-reported measures of linguistic skills with automatic LENA measures. The findings reported in this study support the claim that LENA data are comparable to data retrieved from direct observations, conducted by a researcher on the same week of the recording, and with data from parental questionnaires on linguistic skills. This last result, and especially the relationship between the CVC and the PRISE questionnaire, is in line with Levin-Asher et al.’s Reference Levin-Asher, Segal and Kishon-Rabin2022 study (showing a similar correlation index between the same variables). Although Levin-Asher’s study considered Arabic Reference Levin-Asher, Segal and Kishon-Rabin2022, their results converge with our finding, in suggesting that, the more children vocalize, the higher they score on the PRISE test. Moreover, our study highlights a relationship not only with the PRISE questionnaire, but also between a specific section of the Vocal Reactivity Scale of the Infant Behaviour Questionnaire, i.e., the Vocal Reactivity Scale during play (i.e., how much the child talks when playing with the caregiver), the CVC and the CTC. Thus, the more children talk or the more they are involved in conversational turns as measured by LENA, the more they exhibit vocalisations in their daily play as reported by parents. This result is consistent with evidence showing links between quality of speech, in terms of turn taking, and the child’s language skills at the same age (Ferjan Ramírez et al., Reference Ferjan Ramírez, Lytle and Kuhl2020), or in their later language development (Donnelly et al., Reference Donnelly and Kidd2021; Romeo et al., Reference Romeo, Leonard, Grotzinger, Robinson, Takada, Mackey, Scherer, Rowe, West and Gabrieli2021). Our finding shows that children who are more involved in conversational turns during a typical day with their main caregiver are the same who were perceived as more talkative and linguistically active in play situations from their caregivers. It is interesting to underline that the number of conversational turns is related only to parent’s perception of vocal reactivity during play, not in the other situations included in the IBQ (feeding; washing and dressing; daily activities). This could be related to the child’s higher vocal productivity during this kind of activity or to mother’s higher focus on conversations during play. However, this is only a speculative hypothesis, since we do not have a measure to demonstrate it.
Finally, our most remarkable result regards the relationship with the data from the naturalistic observation. Concurrent validity is shown between LENA estimations (CVC) and direct naturalistic observations, i.e., analyses of linguistic skills based on video samples recording the spontaneous and ecological interaction between participants and caregivers. Importantly, this means that the LENA can be taken as a reliable and valid tool to automatically provide measures of vocal productions, thus reducing the demanding task to transcribe and analyse video observations for future studies. This point brings a concrete methodological contribution for studies examining language development, showing that data that are automatically retrieved from the LENA device can be immediately and easily used by both researchers and experts in education to sketch out a child’s language skills.
Conclusion
In summary, this study confirms that the LENA recording system is a useful, valid and reliable tool to automatically analyse some aspects of children’s environment and of child-adult verbal communication. Increasing interest has emerged, in studies on language development, regarding environmental characteristics considered as important factors for developmental outcomes. The LENA system gives the possibility to easily and reliably assess, in naturalistic settings, quantitative aspects of the child’s vocal production and of the linguistic input children are exposed to. Furthermore, LENA gives the possibility to collect data without the presence of the researcher, an aspect which became all the more relevant during the Covid-19 pandemic period, when direct contacts between people were limited. Another advantage is the simple use of the device that makes it adequate also for families with special needs or with low SES, and in varied contexts. However, the hardware and software of the LENA device also come with some limitations. Above all, one can consider the fact that, in the estimation of children’s production skills, qualitative features of the recorded samples (e.g., indexes of phonetic or lexical diversity, as measured using token versus type ratios) are not automatically computed. In effect, one automatic LENA assessment giving qualitative information on children’s production exists: the Automated Vocal Assessment (AVA), but this index has not been as commonly used as the other measures – thus it is not exploitable in the context of our study on Italian. Furthermore, since LENA counts are extracted using audio recordings, no information is reported about nonverbal communication (e.g., gestures).
The present study offers a first contribution about the validity of the LENA system with Italian children. Our results provide a positive evaluation of the device and encourage further research on the relationship between LENA automatic estimations and direct and indirect language measures. Most notably, analyses on the concurrent validity of the LENA system could be conducted in a longitudinal perspective or extended to different socio-cultural groups of participants.
Acknowledgements
The study was possible thanks to a collaboration established between the University of Verona (SLD-Lab) and LABEX EFL (ANR-10-LABX-0083) that funded a mobility grant.
The authors are grateful to Marica Saffiotti and Giada Puiatti for their help in transcribing and coding. A special thanks to all the families who participated in the study.
Competing interest
We have no known conflict of interest to disclose.
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