A new method of text-independent speaker recognition

Higgins, Alan L.; Wohlford, R.

doi:10.1109/icassp.1986.1168975

Cited by 12 publications

(4 citation statements)

References 5 publications

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“…During training, each speaker's mean vector (67) and covariance matrix (23) are computed and stored as a model. During testing, the recursive mean (24) and recursive covariance (25) are computed and compared with the stored models.…”

Section: A New Speaker-recognition Systemmentioning

confidence: 99%

Speaker recognition: a tutorial

1997

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This paper gives an overview of speaker recognition systems. Speaker recognition is the task of automatically recognizing who is speaking by identifying an unknown speaker among several reference speakers using speaker-specific information included in speech waves. The different classification of speaker recognition and speech processing techniques required for performing the recognition task are discussed. The basic modules of a speaker recognition system are outlined and discussed. Some of the techniques required to implement each module of the system were discussed and others are mentioned. The methods were also compared with one another. Finally, this paper concludes by giving a few research trends in speaker recognition for some years to come.

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Section: A New Speaker-recognition Systemmentioning

confidence: 99%

Speaker recognition: a tutorial

1997

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“…On the other hand, some approaches do not rely on explicit extraction of dynamic features during the paramaterization. Concatenating successive instantaneous feature vectors is a solution investigated in [23,25,27]. In this case, the extended feature vectors convey information of both static and dynamic nature.…”

Section: Fundamentalsmentioning

confidence: 99%

AMIRAL: A Block-Segmental Multirecognizer Architecture for Automatic Speaker Recognition

Fredouille

Bonastre

Merlin

2000

Digital Signal Processing

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“…The speaker recognizers developed so far [2,3,4,5,6,7,9,8,10,11,12] can be broadly classified into text dependent and independent systems. Text dependent systems use a specially designed utterance, whereas text independent systems operate on previously unknown speech utterances.…”

Section: Introductionmentioning

confidence: 99%

“…However, this problem can be overcome by using phonemes. Few phoneme-based speaker recognition systems have been developed [7,12]. One of these [12] uses linear-predictive-coding (LPC) cepstral coefficients as features for a quadratic classifier.…”

Section: Introductionmentioning

confidence: 99%

Text-independent speaker identification system based on adaptive wavelets

Kadambe

Srinivasan

1994

Wavelet Applications

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In this paper, we describe a text independent, phoneme based speaker identification system which uses adaptive wavelets to model the phonemes. This system identifies a speaker by modeling a very short segment of phonemes and then by clustering all the phonemes belonging to the same speaker into one class. The classification is achieved by using a two layer feed forward neural network classifier. The performance of this speaker identification system is demonstrated by considering the phonemes that were extracted from various sentences spoken by three speakers in the TIMIT acoustic-phonetic speech corpus.

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A new method of text-independent speaker recognition

Cited by 12 publications

References 5 publications

Speaker recognition: a tutorial

Speaker recognition: a tutorial

AMIRAL: A Block-Segmental Multirecognizer Architecture for Automatic Speaker Recognition

Text-independent speaker identification system based on adaptive wavelets

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