Robust Acoustic Speech Feature Prediction from Noisy Mel-Frequency Cepstral Coefficients

Milner, Ben and Darch, Jonathan (2010) Robust Acoustic Speech Feature Prediction from Noisy Mel-Frequency Cepstral Coefficients. IEEE Transactions on Audio, Speech, and Language Processing, 19 (2). pp. 338-347. ISSN 1558-7916

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This paper examines the effect of applying noise compensation to acoustic speech feature prediction from noisy mel-frequency cepstral coefficient (MFCC) vectors within a distributed speech recognition architecture. An acoustic speech feature (comprising fundamental frequency, formant frequencies, speech/nonspeech classification, and voicing classification) is predicted from an MFCC vector in a maximum a posteriori (MAP) framework using phoneme-specific or global models of speech. The effect of noise is considered and three different noise compensation methods, that have been successful in robust speech recognition, are integrated within the MAP framework. Experiments show that noise compensation can be applied successfully to prediction with best performance given by a model adaptation method that performs only slightly worse than matched training and testing. Further experiments consider application of the predicted acoustic features to speech reconstruction. A series of human listening tests show that the predicted features are sufficient for speech reconstruction and that noise compensation improves speech quality in noisy conditions.

Item Type: Article
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: Vishal Gautam
Date Deposited: 07 Mar 2011 13:17
Last Modified: 09 Jun 2022 13:51
DOI: 10.1109/TASL.2010.2047811

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