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Acoustic, phonetic, and discriminative approaches to automatic language identification

Summary

Formal evaluations conducted by NIST in 1996 demonstrated that systems that used parallel banks of tokenizer-dependent language models produced the best language identification performance. Since that time, other approaches to language identification have been developed that match or surpass the performance of phone-based systems. This paper describes and evaluates three techniques that have been applied to the language identification problem: phone recognition, Gaussian mixture modeling, and support vector machine classification. A recognizer that fuses the scores of three systems that employ these techniques produces a 2.7% equal error rate (EER) on the 1996 NIST evaluation set and a 2.8% EER on the NIST 2003 primary condition evaluation set. An approach to dealing with the problem of out-of-set data is also discussed.
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Summary

Formal evaluations conducted by NIST in 1996 demonstrated that systems that used parallel banks of tokenizer-dependent language models produced the best language identification performance. Since that time, other approaches to language identification have been developed that match or surpass the performance of phone-based systems. This paper describes and evaluates three...

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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 dialect identification of extemporaneous, conversational, Latin American Spanish Speech

Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, Vol. 2, ICASSP, 7-10 May 1996, pp. 777-780.

Summary

A dialect identification technique is described that takes as input extemporaneous, conversational speech spoken in Latin American Spanish and produces as output a hypothesis of the dialect. The system has been trained to recognize Cuban and Peruvian dialects of Spanish, but could be extended easily to other dialects (and languages) as well. Building on our experience in automatic language identification, the dialect-ID system uses an English phone recognizer trained on the TIMIT corpus to tokenize training speech spoken in each Spanish dialect. Phonotactic language models generated from this tokenized training speech are used during testing to compute dialect likelihoods for each unknown message. This system has an error rate of 16% on the Cuban/Peruvian two-alternative forced-choice test. We introduce the new "Miami" Latin American Spanish speech corpus that is capable of supporting our research into the future.
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Summary

A dialect identification technique is described that takes as input extemporaneous, conversational speech spoken in Latin American Spanish and produces as output a hypothesis of the dialect. The system has been trained to recognize Cuban and Peruvian dialects of Spanish, but could be extended easily to other dialects (and languages)...

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