Automatic 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 languages or dialects with a limited amount of orthographically transcribed speech data. Our system is based on language-independent syllabification of speech, followed by a language-dependent mapping from syllable counts (and a number of other acoustic features) to the corresponding word count estimates. We evaluate our system on samples from daylong infant recordings from six different corpora consisting of several languages and socioeconomic environments, all manually annotated with the same protocol to allow direct comparison. We compare a number of alternative techniques for the two key components in our system: speech activity detection and automatic syllabification of speech. As a result, we show that our system can reach relatively consistent WCE accuracy across multiple corpora and languages (with some limitations). In addition, the system outperforms LENA on three of the four corpora consisting of different varieties of English. We also demonstrate how an automatic neural network-based syllabifier, when trained on multiple languages, generalizes well to novel languages beyond the training data, outperforming two previously proposed unsupervised syllabifiers as a feature extractor for WCE.

We present "DiViMe", an open-source virtual machine aimed at packaging speech technology for real-life data, and developed in the context of the "Analyzing Children's Language Environments across the World" Project. This first release focuses on Speech Activity Detection, Speaker Diarization, and their evaluation. The present paper introduces the set of included tools and the current workflow, which is focused on making minimal assumptions regarding users' technical skills. Additionally, we show how the current DiViMe tools fare against three sets of challenging data. In a first experiment, we look at performance with samples extracted from daylong recordings gathered using the LENA TM system from English-learning children. We find that the performance of the tools currently in DiViMe is not far from that achieved by the LENA TM proprietary software. In a second experiment, we generalize to other samples of child-centered daylong files, gathered with non-LENA TM hardware from non-English-learning children, showing that performance does not degrade in this condition. Finally, we report on performance in the DiHARD 2018 Challenge Test Data. Originally conceived in the "Speech Recognition Virtual Kitchen", DiViMe is a promising platform for packaging speech technology tools for widespread re-use, with potential impact on both fundamental and applied speech and language research.

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