Can back-ends be more robust than front-ends? Investigation over the Aurora-2 database
Alexis Bernard, Yifan Gong, Xiaodong Cui · 2004
We present a back-end solution developed at Texas Instruments for noise robust speech recognition. The solution consists of three techniques: 1) a joint additive and convolutive noise compensation (JAC) which adapts speech acoustic models; 2) an enhanced channel estimation procedure which extends JAC performance towards lower SNR ranges; 3) an N-pass decoding algorithm. The performance of the proposed back-end is evaluated on the Aurora-2 database. With 20% fewer model parameters and without the need for the second order derivative of the recognition features, the performance of the proposed solution is 91.86%, which outperforms that of the ETSI advanced front-end standard (88.19%) by more than 30% relative word error rate reduction.