| Paper: | SLP-L8.6 | ||
| Session: | Efficient Techniques for LVCSR | ||
| Time: | Thursday, May 18, 15:40 - 16:00 | ||
| Presentation: | Lecture | ||
| Topic: | Speech and Spoken Language Processing: Distributed Speech Recognition | ||
| Title: | Multi-Frame GMM-based Block Quantisation for Distributed Speech Recognition Under Noisy Conditions | ||
| Authors: | Stephen So, Kuldip Paliwal, Griffith University, Australia | ||
| Abstract: | In this paper, we report on the recognition accuracy of the multi-frame GMM-based block quantiser for the coding of MFCC features in a distributed speech recognition framework under varying noise conditions. All experiments were performed using the ETSI Aurora-2 connected-digits recognition task. For comparison, we have also investigated other quantisation schemes such as the memoryless GMM-based block quantiser, the unconstrained vector quantiser, and non-uniform scalar quantisers. The results show that the rate-distortion efficiency of the quantiser is a factor in determining the level of recognition accuracy at low to medium levels of additive noise. For high levels of additive noise, the influence of rate-distortion efficiency diminishes and the recognition accuracy becomes dependent on the recognition features. | ||
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