http://cs.uef.fi/sipu/pub/SRE-review-Kinnunen-and-Li.pdf

An overview of text-independent speaker recognition: From features to supervectors

The National Institute of Standards and Technology (NIST) has coordinated annual scientific evaluations of text-independent speaker recognition since 1996. These evaluations aim to provide important contributions to the direction of research efforts and the calibration of technical capabilities. They are intended to be of interest to all researchers working on the general problem of text-independent speaker recognition. To this end, the evaluations are designed to be simple, fully supported, accessible and focused on core technology issues. The evaluations have focused primarily on speaker detection in the context of conversational telephone speech. More recent evaluations have also included related tasks, such as speaker segmentation, and have used data in addition to conversational telephone speech. The evaluations are designed to foster research progress, with the objectives of:

The NIST speaker recognition evaluation program

In this paper we study robust speaker recognition in far-field microphone situations such as meeting scenarios. By applying reverberation compensation and feature warping we achieved significant improvements under mismatched training-testing conditions. To capture useful information from multiple distant microphones, two approaches for multiple channel combination are investigated. This leads to 84.1 % and 78.1% relative improvements on the distant microphone database. Furthermore, we tested the resulting system on the ICSI Meeting Corpus. The improvements are also very high on this task, which indicates that our system is robust to changing conditions in a remote microphone setting

/pdf/far-field-speaker-recognition-3s11t5kmzu.pdf

Far-Field Speaker Recognition

This paper describes improvements to an innovative high-performance speaker recognition system. Recent experiments showed that with sufficient training data phone strings from multiple languages are exceptional features for speaker recognition. The prototype phonetic speaker recognition system used phone sequences from six languages to produce an equal error rate of 11.5% on Switchboard-I audio files. The improved system described in this paper reduces the equal error rate to less then 4%. This is accomplished by incorporating gender-dependent phone models, pre-processing the speech files to remove cross-talk, and developing more sophisticated fusion techniques for the multi-language likelihood scores.

Gender-dependent phonetic refraction for speaker recognition

In this paper, we study robust speaker recognition in far-field microphone situations. Two approaches are investigated to improve the robustness of speaker recognition in such scenarios. The first approach applies traditional techniques based on acoustic features. We introduce reverberation compensation as well as feature warping and gain significant improvements, even under mismatched training-testing conditions. In addition, we performed multiple channel combination experiments to make use of information from multiple distant microphones. Overall, we achieved up to 87.1% relative improvements on our Distant Microphone database and found that the gains hold across different data conditions and microphone settings. The second approach makes use of higher-level linguistic features. To capture speaker idiosyncrasies, we apply n-gram models trained on multilingual phone strings and show that higher-level features are more robust under mismatching conditions. Furthermore, we compared the performances between multilingual and multiengine systems, and examined the impact of a number of involved languages on recognition results. Our findings confirm the usefulness of language variety and indicate a language independent nature of this approach, which suggests that speaker recognition using multilingual phone strings could be successfully applied to any given language.

/pdf/far-field-speaker-recognition-4fvz1c7lyq.pdf

This paper introduces a novel language-independent speaker-recognition system based on differences in dynamic realization of phonetic features (i.e., pronunciation) between speakers rather than spectral differences in voice quality. The system exploits phonetic information from six languages to perform text independent speaker recognition. All experiments were performed on the NIST 2001 Speaker Recognition Evaluation Extended Data Task. Recognition results are provided for unigram, bigram, and trigram models. Performance for each of the three models is examined for phones from each individual language and the final multilanguage fused system. Additional fusion experiments demonstrate that speaker recognition capability is maintained even without phonetic information in the language of the speaker.

J. Herndndez-Cordero

Papers

Phonetic speaker recognition