Uncertainty propagation for noise robust speaker recognition: the case of NIST-SRE

Abstract

Uncertainty propagation is an established approach to handle noisy and reverberant conditions in automatic speech recognition (ASR), but it has little been studied for speaker recognition so far. Yu et al. recently proposed to propagate uncertainty to the Baum-Welch (BW) statistics without changing the posterior probability of each mixture component. They obtained good results on a small dataset (YOHO) but little improvement on the NIST-SRE dataset, despite the use of oracle uncertainty estimates. In this paper, we propose to modify the computation of the posterior probability of each mixture component in order to obtain unbiased BW statistics. We show that our approach improves the accuracy of BW statistics on the Wall Street Journal (WSJ) corpus, but yields little or no improvement on NIST-SRE again. We provide a theoretical explanation for this that opens the way for more efficient exploitation of uncertainty on NIST-SRE and other large datasets in the future.

An Improved Uncertainty Propagation Method for Robust I-vector Based Speaker Recognition

“…However, these approaches focused on the issue of computing representations with in-sufficient data, caused by utterances with different, possibly short durations [9][10][11][12][13][14][15]. This paper pursues the same line as our preliminary study [8], that considered an epistemic uncertainty propagation approach for noise-robust text-independent speaker verification using a system based on i-vectors [16] and probabilistic linear discriminant analysis (PLDA) [17]. Despite the recent introduction of deep learning based modules in the speaker recognition pipeline, reports of the last NIST Speaker Recognition Evaluation (SRE) in 2016 [18] and the experience in the recent campaign NIST-SRE 2018 1 evidenced that the i-vector-PLDA approach still performs among the best systems of the state-of-the-art.…”

“…To the best of our knowledge, the studies in [7,8] are the only ones exploiting this model for noise and reverberation robustness in i-vector based speaker recognition systems, while other works focused on earlier, now deprecated systems. They proposed two different ways to propagate the uncertainty from the enhanced features to the i-vectors.…”

“…In speaker recognition, the uncertainty propagation approach has gained traction motivated by the uncertain nature of the system pipeline when the application scenario tends to more real-world situations. The necessity of improving the system robustness in noisy environments has inspired the development of epistemic uncertainty approaches for speaker modeling based on Gaussian mixture models [5,6] and, more recently, i-vectors [7,8]. Other work based on the aleatoric uncertainty concept has also given rise to uncertainty propagation approaches for speaker recognition.…”

“…In [8], we proposed a method to estimate and propagate the residual uncertainty after multichannel speech enhancement from the acoustic features to the i-vectors. Specifically, we modified the posterior probability of each Gaussian mixture component to obtain unbiased Baum-Welch (BW) statistics.…”

Speaker Recognition With Random Digit Strings Using Uncertainty Normalized HMM-Based i-Vectors

“…The authors in [25], [26] speed-up the uncertainty propagation method by grouping i-vectors together based on their reliability and by finding a representative posterior covariance matrix for each group. In [27], the authors incorporate the uncertainty associated with front-end features into the i-vector extraction framework. Finally, in [28], an extension of uncertainty decoding using simplified PLDA scoring and modified imputation is proposed.…”