ALICE: An open-source tool for automatic measurement of phoneme, syllable, and word counts from child-centered daylong recordings

Abstract

Recordings captured by wearable microphones are a standard method for investigating young children’s language environments. A key measure to quantify from such data is the amount of speech present in children’s home environments. To this end, the LENA recorder and software—a popular system for measuring linguistic input—estimates the number of adult words that children may hear over the course of a recording. However, word count estimation is challenging to do in a language-independent manner; the relationship between observable acoustic patterns and language-specific lexical entities is far from uniform across human languages. In this paper, we ask whether some alternative linguistic units, namely phone(me)s or syllables, could be measured instead of, or in parallel with, words in order to achieve improved cross-linguistic applicability and comparability of an automated system for measuring child language input. We discuss the advantages and disadvantages of measuring different units from theoretical and technical points of view. We also investigate the practical applicability of measuring such units using a novel system called Automatic LInguistic unit Count Estimator (ALICE) together with audio from seven child-centered daylong audio corpora from diverse cultural and linguistic environments. We show that language-independent measurement of phoneme counts is somewhat more accurate than syllables or words, but all three are highly correlated with human annotations on the same data. We share an open-source implementation of ALICE for use by the language research community, allowing automatic phoneme, syllable, and word count estimation from child-centered audio recordings.

Evaluating the feasibility of an automated classifier for target-child-directed speech from LENA recordings

“…Additionally, while our classifier is open-source, LENA software is not; thus, the ability to use this classifier on researchers' own data requires a substantial cost to purchase the LENA recorders and software. Future work should compare whether our classifiers can be used with open-source speech algorithms (e.g., ALICE; Räsänen et al, 2021) to achieve similar performance in sleep and tCDS/ODS classifiers.…”

Managing, storing, and sharing long-form recordings and their annotations

“…The system interfaces well with extant annotation standards. Currently, ChildProject supports: LENA annotations in .its (Xu et al, 2008); ELAN annotations following the ACLEW DAS template (Casillas et al 2017, imported using Pympi: Lubbers and Torreira 2013; as well as rttm files outputted by ACLEW tools, namely the Voice Type Classifier (VTC) by , the Linguistic Unit Count Estimator (ALICE) by Räsänen et al (2020), and the VoCalisation Maturity Network (VCMNet) by Futaisi et al (2019). Users can also adapt routines for file types or conventions that vary.…”

SCALa: A blueprint for computational models of language acquisition in social context

“…datasets 2020), may provide the ideal starting point for this, since it is cross-cultural and contains images that may allow the identification of nonverbal elements of the social interactions. ACLEW project members have illustrated the importance of coordinated data annotation for developing initial annotations , as well as the usefulness of collaborating with experts of speech technology and machine learning to develop tools that speed up annotation and generalize analyses from the hand-annotated fraction to the day-long scale (Al Futaisi et al, 2019;Le Franc et al, 2018;Räsänen et al, 2020).…”