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Speaker detection and tracking for telephone transactions

Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP, 13-17 May 2002, pp. 129-132.

Summary

As ever greater numbers of telephone transactions are being conducted solely between a caller and an automated answering system, the need increases for software which can automatically identify and authenticate these callers without the need for an onerous speaker enrollment process. In this paper we introduce and investigate a novel speaker detection and tracking (SDT) technique, which dynamically merges the traditional enrollment and recognition phases of the static speaker recognition task. In this speaker recognition application, no prior speaker models exist and the goal is to detect and model new speakers as they call into the system while also recognizing utterances from the previously modeled callers. New speakers are added to the enrolled set of speakers and speech from speakers in the currently enrolled set is used to update models. We describe a system based on a GMM speaker identification (SID) system and develop a new measure to evaluate the performance of the system on the SDT task. Results for both static, open-set detection and the SDT task are presented using a portion of the Switchboard corpus of telephone speech communications. Static open-set detection produces an equal error rate of about 5%. As expected, performance for SDT is quite varied, depending greatly on the speaker set and ordering of the test sequence. These initial results, however, are quite promising and point to potential areas in which to improve the system performance.
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Summary

As ever greater numbers of telephone transactions are being conducted solely between a caller and an automated answering system, the need increases for software which can automatically identify and authenticate these callers without the need for an onerous speaker enrollment process. In this paper we introduce and investigate a novel...

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The Lincoln speaker recognition system: NIST EVAL2000

Published in:
6th Int. Conf. on Spoken Language, ICSLP, 16-20 October 2000.

Summary

This paper presents an overview of the Lincoln Laboratory systems fielded for the 2000 NIST speaker recognition evaluation (SRE00). In addition to the standard one-speaker detection tasks, this year's evaluation, as in 1999, included multi-speaker spokes dealing with detection, tracking and segmentation. The design approach for the Lincoln system in SRE00 was to develop a set of core one-speaker detection and multi-speaker clustering tools that could be applied to all the tasks. This paper will describe these core systems, how they are applied to the SRE00 tasks and the results they produce. Additionally, a new channel normalization technique known as handset-dependent test-score norm (HTnorm) is introduced.
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Summary

This paper presents an overview of the Lincoln Laboratory systems fielded for the 2000 NIST speaker recognition evaluation (SRE00). In addition to the standard one-speaker detection tasks, this year's evaluation, as in 1999, included multi-speaker spokes dealing with detection, tracking and segmentation. The design approach for the Lincoln system in...

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A study of computation speed-ups of the GMM-UBM speaker recognition system

Published in:
6th European Conf. on Speech Communication and Technology, EUROSPEECH, 5-9 September 1999.

Summary

The Gaussian Mixture Model Universal Background Model (GMM-UBM) speaker recognition system has demonstrated very high performance in several NIST evaluations. Such evaluations, however, are concerned only with classification accuracy. In many applications, system effectiveness must be evaluated in light of both accuracy and execution speed. We present here a number of techniques for decreasing computation. Using data from the Switchboard telephone speech corpus, we show that significant speed-ups can be obtained while sacrificing surprisingly little accuracy. We expect that these techniques, involving lowering model order as well as processing fewer speech frames, will apply equally well to other recognition systems.
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Summary

The Gaussian Mixture Model Universal Background Model (GMM-UBM) speaker recognition system has demonstrated very high performance in several NIST evaluations. Such evaluations, however, are concerned only with classification accuracy. In many applications, system effectiveness must be evaluated in light of both accuracy and execution speed. We present here a number...

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Automatic speaker clustering from multi-speaker utterances

Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, Vol. II, 15-19 March 1999, pp. 817-820.

Summary

Blind clustering of multi-person utterances by speaker is complicated by the fact that each utterance has at least two talkers. In the case of a two-person conversation, one can simply split each conversation into its respective speaker halves, but this introduces error which ultimately hurts clustering. We propose a clustering algorithm which is capable of associating each conversation with two clusters (and therefore two-speakers) obviating the need for splitting. Results are given for two speaker conversations culled from the Switchboard corpus, and comparisons are made to results obtained on single-speaker utterances. We conclude that although the approach is promising, our technique for computing inter-conversation similarities prior to clustering needs improvement.
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Summary

Blind clustering of multi-person utterances by speaker is complicated by the fact that each utterance has at least two talkers. In the case of a two-person conversation, one can simply split each conversation into its respective speaker halves, but this introduces error which ultimately hurts clustering. We propose a clustering...

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Blind clustering of speech utterances based on speaker and language characteristics

Published in:
5th Int. Conf. Spoken Language Processing (ICSLP), 30 November - 4 December 1998.

Summary

Classical speaker and language recognition techniques can be applied to the classification of unknown utterances by computing the likelihoods of the utterances given a set of well trained target models. This paper addresses the problem of grouping unknown utterances when no information is available regarding the speaker or language classes or even the total number of classes. Approaches to blind message clustering are presented based on conventional hierarchical clustering techniques and an integrated cluster generation and selection method called the d* algorithm. Results are presented using message sets derived from the Switchboard and Callfriend corpora. Potential applications include automatic indexing of recorded speech corpora by speaker/language tags and automatic or semiautomatic selection of speaker specific speech utterances for speaker recognition adaptation.
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Summary

Classical speaker and language recognition techniques can be applied to the classification of unknown utterances by computing the likelihoods of the utterances given a set of well trained target models. This paper addresses the problem of grouping unknown utterances when no information is available regarding the speaker or language classes...

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