Damper, R. I.

Finan, R. A.

Sapeluk, A. T.

English

Southampton (e-Prints Soton)

2 4 6 8 10 12 14 161.151.21.251.31.351.4Sub−bandMean score2 4 6 8 10 12 14 160.140.160.180.20.22Sub−bandAverage STDFigure 3: Mean veriﬁcation scores and their standard deviations for each sub-band.Un-normalized ICNIE(%) FA(%) FR(%) EER(%) FA(%) FR(%) EER(%)Wideband 3.33 10.1 8.89 10.0 3.84 1.67 3.66Sub-band 0.56 5.30 4.00 5.19 1.52 0.0 1.39Signiﬁcance￿￿￿￿￿￿￿￿￿￿ NS￿￿￿ NS NS￿Table 3: Comparison of the wideband and sub-band processing results using the unweighted sum rule. (KEY:￿￿￿￿￿ – highly signiﬁcant at 2% level;￿￿￿ – signiﬁcant at 5% level;￿ – marginally signiﬁcant at 10% level; NS– not signiﬁcant.)107811563234431253906 2468101214406080FrequencySub−banddBFigure 9: Smoothed sub-band LP spectra for the voiced frame of speech shown in Fig. 8.24681012 2468101214−1012Cepstral coefficientsSub−bandFigure 10: Sub-band LP cepstral coefﬁcients for the voiced frame of speech shown in Fig. 8.16

A new ASR approach based on independent processing and recombination of partial frequency bands.

Adaptive mixtures of local experts.

An algorithm for vector quantizer design.

An effective sub-band based approach for robust speaker veriﬁcation.

Analytical expressions for critical-band rate and critical bandwidth as a function of frequency.Journal of the Acoustical

Combining evidence from separate speech recognition processes.

Communication theory and physics.

Comparison of background normalization methods for text-independent speaker veriﬁcation.

Computer Speech: Recognition, Compression and Synthesis.

Effectivenessof linear prediction characteristics of the speech wave for automatic speaker identiﬁcation and veriﬁcation.

Enhancements to DTW and VQ decisionalgorithms for speaker recognition.

Equalizing sub-band error rates in speaker recognition.

Evaluation of a vector quantization talker recognition system in text dependent and text independent modes.

Experimental evaluation of features for robust speaker identiﬁcation.

How do humans process and recognize speech?

Impostor cohort selection for score normalisation in speaker veriﬁcation.

Introduction to Discrete-Time Signals and Systems.

J.andJ.S.Mason(1994).Thepre-detectionoferror-proneclassmembersattheenrollmentstage of speaker recognition systems.

Learning from Data.

Likelihood normalization for speaker veriﬁcation using phone- and speakerindependent models.

Linear Prediction of Speech.

Multi-band speech recognition in noisy environments.

Mutual dependence of the octave-band weights in predicting speech intelligibility.

Neural networks and the bias/variance dilemma.

Neural Networks for Pattern Recognition.

Nonparametric Statistics for the Behavioral Sciences.

Normalizations and selection of speech segments for speaker recognition scoring.

On combining classiﬁers.

RASTA processing of speech.

Recent advances in speaker recognition.

Second-order statistical measures for text-independent speaker recognition.

Sheep, goats, lambs and wolves: A statistical analysis of speaker performance in the NIST

Should recognizers have ears?

Signal modeling techniques in speech recognition.

Signal Processing of Speech.

Speaker background models for connected digit password speaker veriﬁcation.

Speaker identiﬁcation and veriﬁcation using Gaussian mixture models.

Speaker recognition – identifying people by their voices.

Speaker recognition using hidden Markov models, dynamic time warping and vector quantisation.

Speaker recognition: A tutorial.

Speaker veriﬁcation over long-distance telephone lines.

Speakerveriﬁcationusing connected words.

Stacked generalization.

Sub-band based recognition of noisy speech.

Sub-band speaker veriﬁcation using dynamic recombinationweights.InProceedingsof5thInternationalConferenceonSpokenLanguageProcessing, ICSLP98,

Subband approach for automatic speaker recognition: optimal division of the frequency domain.

The full-combination sub-bands approach to noise robust HMM/ANN-based ASR.

Theory of communication.

Towards ASR on partially corrupted speech.

Towards the Use of Sub-Band Processing in Automatic Speaker Recognition.

TRAPS – Classiﬁers of temporal patterns.

Visionary speech: looking ahead to practical speechreading systems. In

Improved data modeling for text-dependent speaker recognition using sub-band processing

Improved data modeling for text-dependent speaker recognition using sub-band processing

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