Adaptation for soft whisper recognition using a throat microphone

Autor:	Alex Waibel, Tanja Schultz, Szu-Chen Stan Jou
Předmět:	Upsampling Multivariate statistics Computer science business.industry Throat microphone Speech recognition Word error rate Pattern recognition Adaptation (eye) Sigmoid function Artificial intelligence Filter (signal processing) business
Zdroj:	Scopus-Elsevier INTERSPEECH
Popis:	This paper describes various adaptation methods applied to recognizing soft whisper recorded with a throat microphone. Since the amount of adaptation data is small and the testing data is very different from the training data, a series of adaptation methods is necessary. The adaptation methods include: maximum likelihood linear regression, feature-space adaptation, and re-training with downsampling, sigmoidal low-pass filter, or linear multivariate regression. With these adaptation methods, the word error rate improves from 99.3% to 32.9%.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2183a6c67d4bfa810403b7035f8e97fe http://www.scopus.com/inward/record.url?eid=2-s2.0-84890487256&partnerID=MN8TOARS Zobrazit plný text záznamu