Speaker recognition

About: Speaker recognition is a research topic. Over the lifetime, 14990 publications have been published within this topic receiving 310061 citations.

State-of-the-Art Speaker Recognition for Telephone and Video Speech: The JHU-MIT Submission for NIST SRE18.

Abstract: We present a condensed description of the joint effort of JHUCLSP, JHU-HLTCOE, MIT-LL., MIT CSAIL and LSE-EPITA for NIST SRE18. All the developed systems consisted of xvector/i-vector embeddings with some flavor of PLDA backend. Very deep x-vector architectures–Extended and Factorized TDNN, and ResNets– clearly outperformed shallower xvectors and i-vectors. The systems were tailored to the video (VAST) or to the telephone (CMN2) condition. The VAST data was challenging, yielding 4 times worse performance than other video based datasets like Speakers in the Wild. We were able to calibrate the VAST data with very few development trials by using careful adaptation and score normalization methods. The VAST primary fusion yielded EER=10.18% and Cprimary=0.431. By improving calibration in post-eval, we reached Cprimary=0.369. In CMN2, we used unsupervised SPLDA adaptation based on agglomerative clustering and score normalization to correct the domain shift between English and Tunisian Arabic models. The CMN2 primary fusion yielded EER=4.5% and Cprimary=0.313. Extended TDNN x-vector was the best single system obtaining EER=11.1% and Cprimary=0.452 in VAST; and 4.95% and 0.354 in CMN2.

Method and apparatus for automated speaker parameters adaptation in a deployed speaker verification system

Abstract: Typical speaker verification systems usually employ speakers' audio data collected during an enrollment phase when users enroll with the system and provide respective voice samples. Due to technical, business, or other constraints, the enrollment data may not be large enough or rich enough to encompass different inter-speaker and intra-speaker variations. According to at least one embodiment, a method and apparatus employing classifier adaptation based on field data in a deployed voice-based interactive system comprise: collecting representations of voice characteristics, in association with corresponding speakers, the representations being generated by the deployed voice-based interactive system; updating parameters of the classifier, used in speaker recognition, based on the representations collected; and employing the classifier, with the corresponding parameters updated, in performing speaker recognition.

High quality voice morphing

Abstract: Voice morphing is a technique for modifying a source speaker's speech to sound as if it was spoken by some designated target speaker. Most of the recent approaches to voice morphing apply a linear transformation to the spectral envelope and pitch scaling to modify the prosody. Whilst these methods are effective, they also introduce artifacts arising from the effects of glottal coupling, phase incoherence, unnatural phase dispersion and the high spectral variance of unvoiced sounds. A practical voice morphing system must account for these if high audio quality is to be preserved. This paper describes a complete voice morphing system and the enhancements needed for dealing with the various artifacts, including a novel method for synthesising natural phase dispersion. Each technique is assessed individually and the overall performance of the system evaluated using listening tests. Overall it is found that the enhancements significantly improve speaker identification scores and perceived audio quality.

Parametric trajectory models for speech recognition

Abstract: The basic motivation for employing trajectory models for speech recognition is that sequences of speech features are statistically dependent and that the effective and efficient modeling of the speech process will incorporate this dependency. In our previous work we presented an approach to modeling the speech process with trajectories. In this paper we continue our development of parametric trajectory models for speech recognition. We extend our models to include time-varying covariances and describe our approach for defining a metric between speech segments based on trajectory models; it is important in developing mixture models of trajectories.

On the decorrelation of filter-bank energies in speech recognition

Abstract: Cepstral coefficients are widely used in speech recognition. In this paper, we claim that they are not the best way of representing the spectral envelope, at least for some usual speech recognition systems. In fact, cepstrum has several disadvantages: poor physical meaning, need of transformation, and low capacity of adaptation to some recognition systems. In this paper, we propose a new representation that significantly outperforms both mel-cepstrum and LPC-cepstrum techniques in both recognition rate and computational cost. It consists of filtering the frequency sequence of filter-bank energies with an extremely simple filter that equalizes the variance of the cepstral coefficients. Excellent results of the new technique using a continuous observation density HMM recognition system and two very different recognition tasks, connected digits and phone recognition, are presented.