Text-dependent speaker verification under noisy conditions using parallel model combination
Lit Ping Wong, Michael Russell · 2002
In real speaker verification applications, additive or convolutive noise creates a mismatch between training and recognition environments, degrading performance. Parallel model combination (PMC) has been used successfully to improve the noise robustness of hidden Markov model (HMM) based speech recognisers. The paper presents the results of applying PMC to compensate for additive noise in HMM-based text-dependent speaker verification. Speech and noise data were obtained from the YOHO and NOISEX-92 databases respectively. Speaker recognition, equal error rates (EER) are presented for noise-contaminated speech at different signal-to-noise ratios (SNRs) and different noise sources. For example, average EER for speech in operations room noise at 6 dB SNR dropped from approximately 20% un-compensated to less than 5% using PMC. Finally, it is shown that speaker recognition performance is relatively insensitive to the exact value of the parameter that determines the relative amplitudes of the speech and noise components of the PMC model.