Voice identification using nearest-neighbor distance measure

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

doi:10.1109/icassp.1993.319317

Cited by 46 publications

(21 citation statements)

References 6 publications

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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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“…The sound classes are not labeled, so separate training of a segmented is not required. Template based clustering, such as vector quantization [2], [11] and if-nearest neighbor with leader clustering [3], has proven to be very effective for this approach to speaker recognition. In the VQ approach, each speaker is represented by a codebook of spectral templates representing the phonetic sound clusters in his/her speech.…”

Section: Introductionmentioning

confidence: 99%

Text Independent Speaker Identification with Finite Multivariate Generalized Gaussian Mixture Model and Hierarchical Clustering Algorithm

Sailaja¹,

Rao²,

Reddy³

2010

IJCA

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In this paper we propose a Text Independent Speaker Identification with Finite Multivariate Generalized Gaussian Mixture Model with Hierarchical Clustering. Each speaker speech spectra are characterized with a mixture of Generalized Gaussian Distribution includes Gaussian and Laplacian distribution as a particular case. It also includes several of the platy, lepto and meso kurtic shapes of the speech spectra. The speech analysis is done with Mel Frequency Cepstral Coefficients extracted from front end process. Using the EM algorithm the model parameters are estimated. The numbers of acoustic classes associated with each speech spectra are determined through Hierarchical clustering. The performance of the proposed algorithm is studied through experimental evolution with 100 speaker"s data base and found that this algorithm outperforms the existing speaker identification algorithm with GMM. It is also observed that this algorithm performs efficiently even heterogeneous population with small (less than 2 seconds utterances)

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“…In [9], the discrete hidden Markov model was augmented with temporal information for VQ to utilize the transition probabilities between states for speaker recognition. The nearestneighbor classifier was also used for voice identification tasks [10].…”

Section: Introductionmentioning

confidence: 99%

Text-dependent Speaker Recognition using Wavelets and Neural Networks

Lim

Woo

2006

Soft Comput

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An intelligent system for text-dependent speaker recognition is proposed in this paper. The system consists of a wavelet-based module as the feature extractor of speech signals and a neural-network-based module as the signal classifier. The Daubechies wavelet is employed to filter and compress the speech signals. The fuzzy ARTMAP (FAM) neural network is used to classify the processed signals. A series of experiments on text-dependent gender and speaker recognition are conducted to assess the effectiveness of the proposed system using a collection of vowel signals from 100 speakers. A variety of operating strategies for improving the FAM performance are examined and compared. The experimental results are analyzed and discussed.

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Voice identification using nearest-neighbor distance measure

Cited by 46 publications

References 6 publications

Speaker Recognition on Single- and Multispeaker Data

Speaker Recognition on Single- and Multispeaker Data

Text Independent Speaker Identification with Finite Multivariate Generalized Gaussian Mixture Model and Hierarchical Clustering Algorithm

Text-dependent Speaker Recognition using Wavelets and Neural Networks

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