Paper: | SP-P5.8 | ||
Session: | Topics in Speech Coding | ||
Time: | Wednesday, May 19, 09:30 - 11:30 | ||
Presentation: | Poster | ||
Topic: | Speech Processing: Speech Coding | ||
Title: | ENHANCED STANDARD COMPLIANT DISTRIBUTED SPEECH RECOGNITION (AURORA ENCODER) USING RATE ALLOCATION | ||
Authors: | Naveen Srinivasamurthy; University of Southern California | ||
Antonio Ortega; University of Southern California | |||
Shrikanth Narayanan; University of Southern California | |||
Abstract: | This paper proposes modifications to improve the recognition performance obtainable by the ETSI standard distributed speech recognition encoder, Aurora. The proposed modifications are standard compliant, i.e., they require no algorithmic modifications to the Aurora operation. Performance improvements are achieved by more efficiently distributing the available bit budget among the seven (different) 2-dimension vector quantizers (VQs), used by Aurora. Improved bit-allocation to the different sub-vectors used in Aurora is achieved by incorporating the importance towards recognition of each of the sub-vectors into the bit-allocation algorithm. The available bits are efficiently distributed among the sub-vectors by allocating a larger fraction of the available bits to the more important sub-vectors and hence maximizing recognition accuracy. The proposed bit-allocation algorithm is based on a novel mutual information (MI) measure. The MI measure quantifies the information content between a sub-vector and the class label and hence is a good indicator of importance of the coefficient towards recognition. It is shown that the proposed MI based method outperforms both the standard Aurora encoder and an encoder designed using traditional mean square error based bit-allocation. For the TIDIGITS connected digits recognition task a 15.2% relative decrease in word error rate (WER) was possible with the proposed modified MI based Aurora encoder when compared to the recognition performance achieved using the standard Aurora encoder. | ||
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