Compensation of SNR and noise type mismatch using an environmental sniffing based speech recognition solution

Yongjoo Chung, John HL Hansen
2013 EURASIP Journal on Audio, Speech, and Music Processing  
Multiple-model based speech recognition (MMSR) has been shown to be quite successful in noisy speech recognition. Since it employs multiple hidden Markov model (HMM) sets that correspond to various noise types and signal-to-noise ratio (SNR) values, the selected acoustic model can be closely matched with the test noisy speech, which leads to improved performance when compared with other state-of-the-art speech recognition systems that employ a single HMM set. However, as the number of HMM sets
more » ... number of HMM sets is usually limited due to practical considerations as well as effective model selection, acoustic mismatch can still be a problem in MMSR. In this study, we proposed methods to improve recognition performance by mitigating the mismatch in SNR and noise type for an MMSR solution. For the SNR mismatch, an optimal SNR mapping between the test noisy speech and the HMM was determined by experimental investigation. Improved performance was demonstrated by employing the SNR mapping instead of using the estimated SNR of the test noisy speech directly. We also proposed a novel method to reduce the effect of noise type mismatch by compensating the test noisy speech in the log-spectrum domain. We first derive the relation between the log-spectrum vectors in the test and training noisy speech. Since the relation is a non-linear function of the speech and noise parameters, the statistical information regarding the testing log-spectrum vectors was obtained by approximation using vector Taylor series (VTS) algorithm. Finally, the minimum mean square error estimation of the training log-spectrum vectors was used to reduce the mismatch between the training and test noisy speech. By employing the proposed methods in the MMSR framework, relative word error rate reduction of 18.7% and 21.3% was achieved on the Aurora 2 task when compared to a conventional MMSR and multi-condition training (MTR) method, respectively.
doi:10.1186/1687-4722-2013-12 fatcat:muzp2bpoyjentlc3oj2thzx6h4