Towards Noise-Robust Speaker Recognition Using Probabilistic Linear Discriminant Analysis

YUN LEI; L. BURGET; L. FERRER; M. GRACIARENA; N. SCHEFFER

Kyoto

Congreso; IEEE Conference on Acoustics, Speech, and Signal Processing (ICASSP); 2012

IEEE

This work addresses the problem of speaker verification where additive noise is present in the enrollment and testing utterances. We show how the current state-of-the-art frame-work can be effectively used to mitigate this effect. We first look at the degradation a standard speaker verification system is subjected to when presented with noisy speech waveforms. We designed and generated a corpus with noisy conditions, based on the NIST SRE 2008 and 2010 data, built using open-source tools and freely available noise samples. We then show how adding noisy training data in the current i-vector- based approach followed by probabilistic linear discriminant analysis (PLDA) can bring significant gains in accuracy at various signal-to-noise ratio (SNR) levels. We demonstrate that this improvement is not feature-specific as we present positive results for three disparate sets of features: standard mel frequency cepstral coefficients, prosodic polynomial coefficients and maximum likelihood linear regression (MLLR)transforms.