Towards Noise-Robust Speaker Recognition Using Probabilistic Linear Discriminant Analysis

Speaker Recognition, noise, robustness, i-vector, PLDA

We show results on a newly designed noisy corpus for speaker recognition where real recordings of babble noise were added to original NIST SRE clean speech data.

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 framework 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-vectorbased 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.