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The mixer and transcript reading corpora: resources for multilingual, crosschannel speaker recognition research

May 24, 2006
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Conference Paper
Author:
Christopher Cieri
…
Published in:
Proc. Int. Conf. on Language Resources and Evaluation, LREC, 24-26 May 2006, pp. 117-20.
Topic:
human language technology
R&D area:
R&D group:

Summary

This paper describes the planning and creation of the Mixer and Transcript Reading corpora, their properties and yields, and reports on the lessons learned during their development.
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Summary

This paper describes the planning and creation of the Mixer and Transcript Reading corpora, their properties and yields, and reports on the lessons learned during their development.

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The mixer and transcript reading corpora: resources for multilingual, crosschannel speaker recognition research

Combining cross-stream and time dimensions in phonetic speaker recognition

April 6, 2003
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Conference Paper
Author:
Q. Jin
…
Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, Vol. 4, 6-10 April 2003, pp. IV-800 - IV-803.
Topic:
speaker recognition
R&D area:
R&D group:

Summary

Recent studies show that phonetic sequences from multiple languages can provide effective features for speaker recognition. So far, only pronunciation dynamics in the time dimension, i.e., n-gram modeling on each of the phone sequences, have been examined. In the JHU 2002 Summer Workshop, we explored modeling the statistical pronunciation dynamics across streams in multiple languages (cross-stream dimensions) as an additional component to the time dimension. We found that bigram modeling in the cross-stream dimension achieves improved performance over that in the time dimension on the NIST 2001 Speaker Recognition Evaluation Extended Data Task. Moreover, a linear combination of information from both dimensions at the score level further improves the performance, showing that the two dimensions contain complementary information.
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Summary

Recent studies show that phonetic sequences from multiple languages can provide effective features for speaker recognition. So far, only pronunciation dynamics in the time dimension, i.e., n-gram modeling on each of the phone sequences, have been examined. In the JHU 2002 Summer Workshop, we explored modeling the statistical pronunciation dynamics...

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Combining cross-stream and time dimensions in phonetic speaker recognition

Phonetic speaker recognition using maximum-likelihood binary-decision tree models

April 6, 2003
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Conference Paper
Author:
Jiri Navratil
…
Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP, Vol. 4, 6-10 April 2003.
Topic:
speaker recognition
R&D area:
R&D group:

Summary

Recent work in phonetic speaker recognition has shown that modeling phone sequences using n-grams is a viable and effective approach to speaker recognition, primarily aiming at capturing speaker-dependent pronunciation and also word usage. This paper describes a method involving binary-tree-structured statistical models for extending the phonetic context beyond that of standard n-grams (particularly bigrams) by exploiting statistical dependencies within a longer sequence window without exponentially increasing the model complexity, as is the case with n-grams. Two ways of dealing with data sparsity are also studied, namely, model adaptation and a recursive bottom-up smoothing of symbol distributions. Results obtained under a variety of experimental conditions using the NIST 2001 Speaker Recognition Extended Data Task indicate consistent improvements in equal-error rate performance as compared to standard bigram models. The described approach confirms the relevance of long phonetic context in phonetic speaker recognition and represents an intermediate stage between short phone context and word-level modeling without the need for any lexical knowledge, which suggests its language independence.
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Summary

Recent work in phonetic speaker recognition has shown that modeling phone sequences using n-grams is a viable and effective approach to speaker recognition, primarily aiming at capturing speaker-dependent pronunciation and also word usage. This paper describes a method involving binary-tree-structured statistical models for extending the phonetic context beyond that of...

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Phonetic speaker recognition using maximum-likelihood binary-decision tree models

The SuperSID project : exploiting high-level information for high-accuracy speaker recognition

April 6, 2003
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Conference Paper
Author:
Douglas A. Reynolds
…
Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, Vol. 4, 6-10 April 2003, pp. IV-784 - IV-787.
Topic:
speaker recognition
R&D area:
R&D group:

Summary

The area of automatic speaker recognition has been dominated by systems using only short-term, low-level acoustic information, such as cepstral features. While these systems have indeed produced very low error rates, they ignore other levels of information beyond low-level acoustics that convey speaker information. Recently published work has shown examples that such high-level information can be used successfully in automatic speaker recognition systems and has the potential to improve accuracy and add robustness. For the 2002 JHU CLSP summer workshop, the SuperSID project was undertaken to exploit these high-level information sources and dramatically increase speaker recognition accuracy on a defined NIST evaluation corpus and task. This paper provides an overview of the structures, data, task, tools, and accomplishments of this project. Wide ranging approaches using pronunciation models, prosodic dynamics, pitch and duration features, phone streams, and conversational interactions were explored and developed. In this paper we show how these novel features and classifiers indeed provide complementary information and can be fused together to drive down the equal error rate on the 2001 NIS extended data task to 0.2% - a 71% relative reduction in error over the previous state of the art.
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Summary

The area of automatic speaker recognition has been dominated by systems using only short-term, low-level acoustic information, such as cepstral features. While these systems have indeed produced very low error rates, they ignore other levels of information beyond low-level acoustics that convey speaker information. Recently published work has shown examples...

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The SuperSID project : exploiting high-level information for high-accuracy speaker recognition

Gender-dependent phonetic refraction for speaker recognition

January 1, 2002
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Conference Paper
Author:
Walter D. Andrews
…
Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, 13-17 May 2002, Vol. 1, pp. 149-152.
Topic:
speaker recognition
R&D area:
R&D group:

Summary

This paper describes improvement 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 than 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.
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Summary

This paper describes improvement 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%...

READ MORE
Gender-dependent phonetic refraction for speaker recognition
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