Robust Speech Recognition by Combining a Robust Feature Extraction with an Adaptation of HMMs

Konferenz: Sprachkommunikation 2010 - 9. ITG-Fachtagung
06.10.2010 - 08.10.2010 in Bochum, Deutschland

Tagungsband: Sprachkommunikation 2010

Seiten: 4Sprache: EnglischTyp: PDF

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Hirsch, Hans-Günter; Kitzig, Andreas (Department of Electrical Engineering and Computer Science, Niederrhein University of Applied Sciences, Krefeld, Germany)

A method is presented to extract robust features from a noisy speech signal with the intention to improve the performance of an automatic speech recognition system. The processing is based on an adaptive filtering of the short-term spectra where the frequency response of the filter is smoothed with a cepstro-temporal approach. It turns out that the recognition performance is comparable with the performance that can be achieved with a robust feature extraction scheme standardized by ETSI. Looking at the case of a hands-free speech input in a noisy and reverberant environment the recognition rates can be improved further by additionally adapting the HMMs to the acoustic conditions