Title :
Recognition of Reverberant Speech using Full Cepstral Features and Spectral Missing Data
Author :
Palomäki, Kalle J. ; Brown, Guy J. ; Barker, Jon P.
Abstract :
We describe a novel approach to feature combination within the missing data (MD) framework for automatic speech recognition, and show its application to reverberated speech. Likelihoods from a spectral MD classifier are combined with those from a full cepstral feature vector-based recogniser. Even though the performance of the cepstral recogniser is substantially below that of the MD recogniser, the combined recogniser performs better in all conditions. We also describe improvements to the generation of time-frequency masks for the MD recogniser. Our system is compared with a previous approach based on a hybrid MLP-HMM recogniser with MSG and PLP feature vectors. The proposed system has a substantial performance advantage in the most reverberated conditions
Keywords :
cepstral analysis; reverberation; speech recognition; time-frequency analysis; automatic speech recognition; feature vector-based recogniser; full cepstral features; reverberant speech recognition; spectral missing data; time-frequency masks; Additive noise; Automatic speech recognition; Cepstral analysis; Filtering; Hidden Markov models; Noise robustness; Reverberation; Speech coding; Speech recognition; Time frequency analysis;
Conference_Titel :
Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on
Conference_Location :
Toulouse
Print_ISBN :
1-4244-0469-X
DOI :
10.1109/ICASSP.2006.1660014