An instantiable speech biometrics module with natural language interface: implementation in the telephony environment

Navrátil, Jǐŕı; Kleindienst, Jan; Maes, Stéphane

doi:10.1109/icassp.2000.859155

Cited by 6 publications

(2 citation statements)

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“…Speech recognition technologies have been pioneered at IBM since the early '70s with one of the largest groups in the world dedicated to solving problems in speech and language using unique statistical modeling approaches. A long term research effort initiated by Jelinek and continued by Nahamoo, Bahl, Mercer, Picheny, Roukos et al, [3,38,37] has led to IBM's highly successful retail dictation product, ViaVoice. This product is available in 12 different languages.…”

Section: Speech and Handwriting Recognitionmentioning

confidence: 99%

“…Gameplaying programs have traditionally used a combination of evaluation functions to determine how good a particular position is, and effective search techniques to search through a space of possible states. The difficulty in developing a champion-level chess-playing program lies in the facts that first, the state space is so large (since the branching factor or ratio, the number of states that can be reached from a given state of the game, is [30][31][32][33][34][35][36][37][38][39][40] and second, the sophisticated evaluation of chess positions can be difficult to develop and computationally expensive to perform.…”

Section: Chessmentioning

confidence: 99%

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AI at IBM Research

2000

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Report for early dissemination of its contents. In view of the transfer of copyright to the outside publisher, its distribution outside of IBM prior to publication should be limited to peer communications and specific requests. After outside publication, requests should be filled only by reprints or legally obtained copies of the article (e.g. , payment of royalties). Copies may be requested from IBM T.

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Section: Speech and Handwriting Recognitionmentioning

confidence: 99%

Section: Chessmentioning

confidence: 99%

AI at IBM Research

2000

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Conversational Speech Biometrics

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Multigrained modeling with pattern specific maximum likelihood transformations for text-independent speaker recognition

Chaudhari

Navrátil

Maes

2003

IEEE Trans. Speech Audio Process.

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We present a transformation based, multigrained data modeling technique in the context of text independent speaker recognition, aimed at mitigating di culties caused by sparse training and test data. Both identication and veri cation are addressed, where we view the entire population as divided into the target population and its complement, which we refer to as the background population. First, we present our development of maximum likelihood transformation based recognition with diagonally constrained Gaussian mixture models and show its robustness to data scarcity with results on identi cation. Then for each target and background speaker, a multi-grained model is constructed using the transformation based extension as a building block. The training data is labeled with an HMM based phone labeler. We then make use of a graduated phone class structure to train the speaker model at various levels of detail. This structure is a tree with the root node containing all the phones. Subsequent levels partition the phones into increasingly ner grained linguistic classes. This method a ords the use of ne detail where possible, i.e. as re ected in the amount of training data distributed to each tree node. We demonstrate the e ectiveness of the modeling with veri cation experiments in matched and mismatched conditions. Keywords| Speaker recognition, maximum likelihood linear transform, multi-grained modeling.

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An instantiable speech biometrics module with natural language interface: implementation in the telephony environment

Cited by 6 publications

References 7 publications

AI at IBM Research

AI at IBM Research

Conversational Speech Biometrics

Multigrained modeling with pattern specific maximum likelihood transformations for text-independent speaker recognition

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