| Paper: | SP-P16.9 | ||
| Session: | Speech Modeling for Robust Speech Recognition | ||
| Time: | Friday, May 21, 15:30 - 17:30 | ||
| Presentation: | Poster | ||
| Topic: | Speech Processing: Robust Speech Recognition | ||
| Title: | CAN BACK-ENDS BE MORE ROBUST THAN FRONT-ENDS? INVESTIGATION OVER THE AURORA-2 DATABASE | ||
| Authors: | Alexis Bernard; Texas Instruments, Inc. | ||
| Yifan Gong; Texas Instruments, Inc. | |||
| Xiaodong Cui; University of California, Los Angeles | |||
| Abstract: | We present a back-end solution developed at Texas Instruments for noise robust speechrecognition. The solution consists of three techniques: 1) a joint additive andconvolutive noise compensation (JAC) which adapts speech acoustic models, 2) an enhancedchannel estimation procedure which extends JAC performance towards lower SNR ranges, and3) an N-pass decoding algorithm. The performance the proposed back-end is evaluated onthe Aurora-2 database. With 20% less model parameters and without the need for secondorder derivative of the recognition features, the performance of the proposed solution is91.86%, which outperforms that of the ETSI Advanced Front-End standard (88.19%) by morethan 30% relative word error rate reduction. | ||
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