International audience

The past decade has witnessed a significant improvement in speaker recognition (SR) technology in terms of performance with the introduction of the i-vectors framework. Despite these advances, the performance of SR systems considerably suffers in the presence of acoustic nuisances and variabilities. In this paper, we develop a data-driven nuisance compensation technique in the i-vector space without referring to the effects of the targeted nuisances in the temporal domain. This approach is nonparametric as it does not suppose a specific relationship between a “good” version of an i-vector and its corrupted version. Instead, our algorithm models directly the joint distribution of both representations (the good i-vector and its corrupted version) and takes advantage of the reproducibility of acoustic corruptions to generate the corrupted i-vectors. We then build an MMSE estimator that computes an improved version of a corrupted test i-vector, given this joint distribution. Experiments are carried out on NIST SRE 2010 and speakers in the wild databases where the proposed algorithm is used to deal with additive noise and short utterances. Our technique is shown to be efficient, improving the baseline system performance in terms of equal-error rate by up to 70% when used on known test noises and up to 65% in the context of unseen noises using a generic model. It was also proven efficient in the context of duration mismatch reaching up to 40% of relative improvement when used on short utterances using multiple models corresponding to different durations and up to 36% when used on arbitrary duration test segments.

Forensic Voice Comparison (FVC) is increasingly using the likelihood ratio (LR) in order to indicate whether the evidence supports the prosecution (same-speaker) or defender (different-speakers) hypotheses. Nevertheless, the LR accepts some practical limitations due both to its estimation process itself and to a lack of knowledge about the reliability of this (practical) estimation process. It is particularly true when FVC is considered using Automatic Speaker Recognition (ASR) systems. Indeed, in the LR estimation performed by ASR systems, different factors are not considered such as speaker intrinsic characteristics, denoted “speaker factor”, the amount of information involved in the comparison as well as the phonological content and so on. This article focuses on the impact of phonological content on FVC involving two different speakers and more precisely the potential implication of a specific phonemic category on wrongful conviction cases (innocents are send behind bars). We show that even though the vast majority of speaker pairs (more than 90%) are well discriminated, few pairs are difficult to distinguish. For the “best” discriminated pairs, all the phonemic content play a positive role in speaker discrimination while for the “worst” pairs, it appears that nasals have a negative effect and lead to a confusion between speakers.

18th Annual Conference of the International Speech Communication Association, INTERSPEECH 2017, Stockholm, Sweden, 20-24 August 2017

In forensic voice comparison, it is strongly recommended to follow Bayesian paradigm. In this paradigm, the strength of the forensic evidence is summarized by a likelihood ratio (LR). The LR magnitude quantifies the strength of the evidence: far from unity for a meaningful LR (a LR which supports strongly one of the hypothesis); close to unity when the evidence is next to useless. Despite this nice theoretical aspect, the LR does not embed the reliability of its estimation process itself. And, in various cases, a lack in reliability inside the estimation process is able to destroy the reliability of the resulting LR. It is particularly true when voice comparison is considered, as Speaker Recognition (SR) systems are outputting a score in all situations regardless of the case specific conditions. Furthermore, SR systems use different normalization steps to see their scores as LR and these normalization steps are clearly a potential source of bias. Consequently, a complete view of reliability should be taken into account for forensic voice comparison. This article focuses on one part of this question, the "speaker factor", the characteristics and the behaviors of the two speakers involved in a voice comparison trial.

Forensic Voice Comparison (FVC) is increasingly using the likelihood ratio (LR) in order to indicate whether the evidence supports the prosecution (same-speaker) or defender (different-speakers) hypotheses. In addition to support one hypothesis, the LR provides a theoretically founded estimate of the relative strength of its support. Despite this nice theoretical aspect, the LR accepts some practical limitations due both to its estimation process itself and to a lack of knowledge about the reliability of this (practical) estimation process. In a large set of situations, a lack in reliability at the estimation process level potentially destroys the reliability of the resulting LR. It is particularly true when automatic FVC is considered, as Automatic Speaker Recognition (ASpR) systems are outputting a score in all situations regardless of the case specific conditions. Furthermore, ASpR systems use different normalization steps to see their scores as LR and these normalization steps are potential sources of bias. In the LR estimation done by ASpR systems, different factors are not taken into account such as the amount of information involved in the comparison, the phonemic content and finally the speaker intrinsic characteristics, denoted here “speaker factor”. Consequently, a more complete view of reliability seems to be a mandatory point for FVC, even if a LR-like approach is used. This article focuses on the impact of phonemic content on FVC performance and variability. The experimental part is using FABIOLE database. This database is dedicated to this kind of studies and allows to examine both inter-speaker variability and intra-speaker variability. The results demonstrate the importance of the phonemic content and highlight interesting differences between inter-speakers effects and intra-speaker's ones.

State-of-the-art speaker recognition systems performance degrades considerably in noisy environments even though they achieve very good results in clean conditions. In order to deal with this strong limitation, we aim in this work to remove the noisy part of an i-vector directly in the i-vector space. Our approach offers the advantage to operate only at the i-vector extraction level, letting the other steps of the system unchanged. A maximum a posteriori (MAP) procedure is applied in order to obtain clean version of the noisy i-vectors taking advantage of prior knowledge about clean i-vectors distribution. To perform this MAP estimation, Gaussian assumptions over clean and noise i-vectors distributions are made. Operating on NIST 2008 data, we show a relative improvement up to 60% compared with baseline system. Our approach also outperforms the “multi-style” backend training technique. The efficiency of the proposed method is obtained at the price of relative high computational cost. We present at the end some ideas to improve this aspect.