Improved voice identification using a nearest-neighbor distance measure

Bahler, Lawrence G.; Porter, J.; Higgins, Alan L.

doi:10.1109/icassp.1994.389291

Cited by 8 publications

(3 citation statements)

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“…Dragon is not the first site to explore nonparametric approaches to speaker recognition (see, e.g., [12,13]), but most other approaches have treated frames as independent, neglecting sequential information. It is interesting to see how the SNP system compares to a simpler nonparametric system which, like the parametric GMM system, does not use sequential information nor LVCSR techniques.…”

Section: The Snp Systemmentioning

confidence: 99%

Speaker Recognition on Single- and Multispeaker Data

Weber¹,

Peskin²,

Newman³

et al. 2000

Digital Signal Processing

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Section: The Snp Systemmentioning

confidence: 99%

Speaker Recognition on Single- and Multispeaker Data

Weber¹,

Peskin²,

Newman³

et al. 2000

Digital Signal Processing

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“…To counter misalignments, arising from change in speaking rate, for example, temporal alignment using dynamic time warping (DTW) is often applied during pattern matching. The reference patterns may be taken directly from the original pattern space; this approach is used in k-nearest-neighbor (kNN) classifiers [4]. Alternatively, the reference patterns may represent a compressed pattern space, typically obtained through vector averaging.…”

Section: A Parametric Modelsmentioning

confidence: 99%

A review of speech-based bimodal recognition

Chibelushi

Deravi

Mason

2002

IEEE Trans. Multimedia

185

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Speech recognition and speaker recognition by machine are crucial ingredients for many important applications such as natural and flexible human-machine interfaces. Most developments in speech-based automatic recognition have relied on acoustic speech as the sole input signal, disregarding its visual counterpart. However, recognition based on acoustic speech alone can be afflicted with deficiencies that preclude its use in many real-world applications, particularly under adverse conditions. The combination of auditory and visual modalities promises higher recognition accuracy and robustness than can be obtained with a single modality. Multimodal recognition is therefore acknowledged as a vital component of the next generation of spoken language systems. This paper reviews the components of bimodal recognizers, discusses the accuracy of bimodal recognition, and highlights some outstanding research issues as well as possible application domains.

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“…Another generalization of the NN rule involves the explicit incorporation of distances [28,29]. Consider speaker model i containing the training set Ti = (t1; t2; : : :) and a set of test feature vectors X = (x1; x2; : : :).…”

Section: Nearest Neighbormentioning

confidence: 99%

Speaker recognition—general classifier approaches and data fusion methods

Ramachandran

Farrell²,

Ramachandran

et al. 2002

Pattern Recognition

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Speaker recognition refers to the concept of recognizing a speaker by his=her voice or speech samples. Some of the important applications of speaker recognition include customer veriÿcation for bank transactions, access to bank accounts through telephones, control on the use of credit cards, and for security purposes in the army, navy and airforce. This paper is purely a tutorial that presents a review of the classiÿer based methods used for speaker recognition. Both unsupervised and supervised classiÿers are described. In addition, practical approaches that utilize diversity, redundancy and fusion strategies are discussed with the aim of improving performance.

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Improved voice identification using a nearest-neighbor distance measure

Cited by 8 publications

References 1 publication

Speaker Recognition on Single- and Multispeaker Data

Speaker Recognition on Single- and Multispeaker Data

A review of speech-based bimodal recognition

Speaker recognition—general classifier approaches and data fusion methods

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