Lei, Y., Burget, L., Ferrer, L., Graciarena, M., & Scheffer, N. (2012, March). Towards noise-robust speaker recognition using probabilistic linear discriminant analysis. In 2012 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 4253-4256). 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 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.