| Paper: | SPTM-P13.11 | ||
| Session: | Detection and Classification | ||
| Time: | Friday, May 21, 15:30 - 17:30 | ||
| Presentation: | Poster | ||
| Topic: | Signal Processing Theory and Methods: Detection, Estimation, and Class. Thry & Apps. | ||
| Title: | VOICE ACTIVITY DETECTION WITH NOISE REDUCTION AND LONG-TERM SPECTRAL DIVERGENCE ESTIMATION | ||
| Authors: | Javier Ramírez; Universidad de Granada | ||
| José C. Segura; Universidad de Granada | |||
| Carmen Benítez; Universidad de Granada | |||
| Ángel de la Torre; Universidad de Granada | |||
| Antonio J. Rubio; Universidad de Granada | |||
| Abstract: | This paper is mainly focussed on an improved voice activity detection algorithm employing long-term signal processing and maximum spectral component tracking. The benefits of this approach has been analyzed in a previous work with clear improvements in speech/non-speech discriminability and speech recognition performance in noisy environments. Two clear aspects are considered in this paper. The first one, which improves the performance of the VAD in low noise conditions, considers an adaptive length frame window to track the long-term spectral components. The second one reduces misclassification errors in high noisy environments by using a noise reduction stage before the long-term spectral tracking. Experimental results show clear improvements over different VAD methods in speech/pause discrimination and speech recognition performance. Particularly, the proposed VAD reported improvements in recognition rate when replaced the VADs of the ETSI Advanced Front-end (AFE) for distributed speech recognition (DSR). | ||
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