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Non-stationary feature extraction for automatic speech recognition

Non-stationary feature extraction for automatic speech recognition,10.1109/ICASSP.2011.5947530,Zoltan Tuske,Pavel Golik,Ralf Schluter,Friedhelm R. Dre

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Non-stationary feature extraction for automatic speech recognition  
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In current speech recognition systems mainly Short-Time Fourier Transform based features like MFCC are applied. Dropping the short-time stationarity assumption of the voiced speech, this paper introduces the non-stationary signal analysis into the ASR framework. We present new acoustic features extracted by a pitch-adaptive Gammatone filter bank. The noise robustness was proved on AURORA 2 and 4 tasks, where the proposed features outperform the standard MFCC. Furthermore, successful combination experiments via ROVER indicate the differences between the new features and MFCC.
Conference: International Conference on Acoustics, Speech, and Signal Processing - ICASSP , pp. 5204-5207, 2011
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