Automatic word count estimation from daylong child-centered recordings in various language environments using language-independent syllabification of speech

Publication date: Available online 14 August 2019Source: Speech CommunicationAuthor(s): Okko Räsänen, Shreyas Seshadri, Julien Karadayi, Eric Riebling, John Bunce, Alejandrina Cristia, Florian Metze, Marisa Casillas, Celia Rosemberg, Elika Bergelson, Melanie SoderstromAbstractAutomatic word count estimation (WCE) from audio recordings can be used to quantify the amount of verbal communication in a recording environment. One key application of WCE is to measure language input heard by infants and toddlers in their natural environments, as captured by daylong recordings from microphones worn by the infants. Although WCE is nearly trivial for high-quality signals in high-resource languages, daylong recordings are substantially more challenging due to the unconstrained acoustic environments and the presence of near- and far-field speech. Moreover, many use cases of interest involve languages for which reliable ASR systems or even well-defined lexicons are not available. A good WCE system should also perform similarly for low- and high-resource languages in order to enable unbiased comparisons across different cultures and environments. Unfortunately, the current state-of-the-art solution, the LENA system, is based on proprietary software and has only been optimized for American English, limiting its applicability. In this paper, we build on existing work on WCE and present the steps we have taken towards a freely available system for WCE that can be adapted to different languag...
Source: Speech Communication - Category: Speech-Language Pathology Source Type: research