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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. WS-852 DIGITAL VOICE REKORDER BEDIENUNGSANLEITUNG Vielen Dank für den Kauf des Digital Voice Rekorders von Olympus. Mit einer Vielzahl an Funktionen können Sie schnell und bequem das Beste aus Ihren Aufnahmen herausholen.
OLYMPUS SONORITY WPS 852 SOFTWARE
In addition, the system outperforms LENA on three of the four corpora consisting of different varieties of English. Die Olympus Sonority Software ergänzt die digitalen Diktiergeräte von Olympus um die Möglichkeit, Audiodateien auf dem PC zu verwalten und zu bearbeiten. As a result, we show that our system can reach relatively consistent WCE accuracy across multiple corpora and languages (with some limitations). We compare a number of alternative techniques for the two key components in our system: speech activity detection and automatic syllabification of speech. 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. 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. 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. 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. 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. Moreover, many use cases of interest involve languages for which reliable ASR systems or even well-defined lexicons are not available.
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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. 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. Automatic word count estimation (WCE) from audio recordings can be used to quantify the amount of verbal communication in a recording environment.