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

Ben Milner, Jonathan Darch

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)338-347
Number of pages10
JournalIEEE Transactions on Audio, Speech, and Language Processing
Volume19
Issue number2
DOIs
Publication statusPublished - 2010

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