| Paper: | SLP-L13.1 | ||
| Session: | Missing Data Methods in Robust Speech Recognition | ||
| Time: | Friday, May 19, 16:30 - 16:50 | ||
| Presentation: | Lecture | ||
| Topic: | Speech and Spoken Language Processing: Model-based robust Speech Recognition | ||
| Title: | RECOGNITION OF REVERBERANT SPEECH USING FULL CEPSTRAL FEATURES AND SPECTRAL MISSING DATA | ||
| Authors: | Kalle J. Palomäki, Helsinki University of Technology, Finland; Guy J. Brown, Jon P. Barker, University of Sheffield, United Kingdom | ||
| 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. | ||
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