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MAXIMUM-LIKELIHOOD-ESTIMATION OF THE PARAMETERS OF DISCRETE FRACTIONALLY DIFFERENCED GAUSSIAN-NOISE PROCESS

IEEE TRANSACTIONS ON SIGNAL PROCESSING 1993年第10期41卷 2977-2990页

作者： DERICHE, M TEWFIK, AH Department of Electrical Engineering University of Minnesota Minneapolis MN USA

A maximum likelihood estimation procedure is constructed for estimating the parameters of discrete fractionally differenced Gaussian noise from an observation set of finite size N. The procedure does not involve the computation of any matrix inverse or determinant. It requires N2/2 + O(N) operations. The expected value of the loglikelihood function for estimating the parameter d of fractionally differenced Gaussian noise (which corresponds to a parameter of the equivalent continuous-time fractional Brownian motion related to its fractal dimension) is shown to have a unique maximum with the range of allowable values of d. The maximum occurs at the true value of d. A Cramer-Rao bound on the variance of any unbiased estimate of d obtained from a finite size observation set is derived. It is shown experimentally that the maximum likelihood estimate of d is unbiased and efficient when finite size data sets are used in the estimation procedure. The proposed procedure is also extended to deal with noisy observations of discrete fractionally differenced Gaussian noise.

关键词： Maximum likelihood estimation Gaussian noise Parameter estimation Brownian motion Fractals Signal processing Speech processing linear predictive coding Filters Motion estimation

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Time-Varying Feature Selection and Classification of Unvoiced Stop Consonants

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IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING 1994年第3期2卷 395-405页

作者： Nathan, Krishna S. Silverman, Harvey F. Brown Univ Div Engn LEMS Providence RI 02912 USA

A feature set that captures the dynamics of formant transitions prior to closure in a VCV environment is used to characterize and classify the unvoiced stop consonants. The feature set is derived from a time-varying, data-selective model for the speech signal. Its performance is compared with that of comparable formant data from a standard delta-LPC-based madel. The different feature sets are evaluated on a database composed of eight talkers. A 40% reduction in classification error rate is obtained by means of the time-varying model. The performance of three different classifiers is discussed. A novel adaptive algorithm, termed learning vector classifier (LVC) is compared with standard K-means and LVQ2 classifiers. LVC is a supervised learning classifier that improves performance by increasing the resolution of the decision boundaries. Error rates obtained for the three-way (p, t, and k) classification task using LVC and the time-varying analysis are comparable to that of techniques that make use of additional discriminating information contained in the burst. Further improvements are expected when an expanded time-varying feature set is utilized, coupled with information from the burst.

关键词： Speech Error analysis linear predictive coding Spatial databases Adaptive algorithm Supervised learning Information analysis Lips Teeth

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R/D optimal linear prediction

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IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING 2000年第6期8卷 646-655页

作者： Prandoni, P Vetterli, M Ecole Polytech Fed Lausanne Lab Commun Audio Visuelle CH-1015 Lausanne Switzerland Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA

A common technique to extend linear prediction to nonstationary signals is time segmentation: the signal is split into small portions and the modelization is carried out locally, The accuracy of the analysis is, however dependent on the window size and on the signal characteristics, so that the problem of finding a good segmentation is crucial to the entire modeling scheme, In this paper, we will present an algorithm which determines the optimal segmentation with respect to a cost function relating prediction error to modeling cost. The proposed approach casts the problem in a rate/distortion (R/D) framework, whereby the segmentation is implicitly computed while minimizing the modelization distortion for a given modelization cost. The algorithm is implemented by means of dynamic programming and takes the form of a trellis-based Lagrangian minimization, The optimal linear predictor, when applied to speech coding, dramatically reduces the number of bits per second devoted to the modeling parameters in comparison to fixed-window schemes.

关键词： dynamic programming linear predictive coding nonstationary signal segmentation rate-distortion

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On-line signature verification using LPC cepstrum and neural networks

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 1997年第1期27卷 148-153页

作者： Wu, QZ Jou, IC Lee, SY TELECOMMUN LABS CHUNGLITAIWAN

In this paper, an on-line signature verification scheme based on linear Prediction coding (LPC) cepstrum and neural networks is proposed. Cepstral coefficients derived from linear predictor coefficients of the writing trajectories are calculated as the features of the signatures. These coefficients are used as inputs to the neural networks. A number of single-output multilayer perceptrons (MLP's), as many as the number of words in the signature, are equipped for each registered person to verify the input signature. If the summation of output values of all MLP's is larger than verification threshold, the input signature is regarded as a genuine signature;otherwise, the input signature is a forgery. Simulations show that this scheme can detect the genuineness of the input signatures from our test database with an error rate as low as 4%.

关键词： Handwriting recognition linear predictive coding Cepstrum Neural networks Cepstral analysis Writing Trajectory Multilayer perceptrons Forgery Testing

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NETWORK-BASED ISOLATED DIGIT RECOGNITION USING VECTOR QUANTIZATION

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IEEE TRANSACTIONS ON ACOUSTICS SPEECH AND SIGNAL PROCESSING 1985年第4期33卷 850-867页

作者： KOPEC, GE BUSH, MA Schlumberger Computer Aided Systems Research Palo Alto CA USA

This paper describes a network-based approach to speaker-independent digit recognition. The digits are modeled by a pronunciation network whose arcs represent classes of acoustic-phonetic segments. Each arc is associated with a matcher for rating an input speech interval as an example of the corresponding segment class. The matchers are based on vector quantization of LPC spectra. Recognition involves finding a minimum quantization distortion path through the network by dynamic programming. The system has been evaluated in an extensive series of speaker-independent isolated digit (one-nine, oh and zero) recognition experiments using a 225-talker. multidialect database developed by Texas Instruments (TI). The best recognizer configurations achieved accuracies of 97-99 percent on the TI database.

关键词： Vector quantization Hidden Markov models Pattern matching Databases Vocabulary Acoustic testing Speech linear predictive coding Dynamic programming Instruments

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AMPLITUDE OPTIMIZATION AND PITCH PREDICTION IN MULTIPULSE CODERS

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IEEE TRANSACTIONS ON ACOUSTICS SPEECH AND SIGNAL PROCESSING 1989年第3期37卷 317-327页

作者： SINGHAL, S ATAL, BS AT&T BELL LABS ACOUST RES DEPTMURRAY HILLNJ 07974 AT&T BELL LABS DIV SPEECH & IMAGING PROC RESMURRAY HILLNJ 07974

Although the multipulse model is conceptually simple, the problem of locating the pulses is computationally complex. The authors discuss the basic multipulse model and describe a procedure to compute the excitation with optimally adjusted amplitudes. The algorithm provides a framework for computing multipulse excitation with varying degrees of optimization and computational complexity. The authors find that speech quality depends on the pulse rate. They also find that for the same quality, female speech requires a higher pulse rate than male speech. The pitch dependence can be reduced and speech quality improved for high-pitched speakers by incorporating long delay prediction in the multipulse model.< >

关键词： Speech synthesis Speech enhancement linear predictive coding Filters Bit rate Speech analysis Signal synthesis predictive models Speech coding White noise

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A split-band predictive coding system at 16 kb/s

A split-band predictive coding system at 16 kb/s

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： J. Welch C. Teacher Aeronutronic Ford Corporation Willow Grove Pa.

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Discriminative training for neural predictive coding applied to speech features extraction

Discriminative training for neural predictive coding applied...

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International Joint Conference on Neural Networks (IJCNN)

作者： M. Chetouani B. Gas J.L. Zarader C. Chavy Laboratoire des Instruments et Systèmes (d'IIe de France (LISIF) Université Paris VI Paris Cedex 05 FRANCE

We present a predictive neural network called neural predictive coding (NPC). This model is used for nonlinear discriminant features extraction applied to phoneme recognition. We validate the nonlinear prediction improvement of the NPC model. We also, present a new extension of the NPC model: NPC-3. In order to evaluate the performances of the NPC-3 model, we carried out a study of Darpa-Timit phonemes (in particular /b/, /d/, /g/ and /p/, /t/, /q/ phonemes) recognition. Comparisons with traditional coding methods are presented. We also show how an adaptative constraint allows improvements on the recognition task.

关键词： predictive coding Speech coding Feature extraction predictive models Nonlinear filters linear predictive coding Neural networks Speech recognition Mel frequency cepstral coefficient Instruments

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Vector predictive quantization of the spectral parameters for low rate speech coding

Vector predictive quantization of the spectral parameters fo...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Y. Shoham Dpt. of Signal Processing Research AT and T Bell Laboratories Inc. Murray Hill NJ USA

Vector predictive Quantization (VPQ) is proposed for coding the short-term spectral envelope of speech. The proposed VPQ scheme predicts the current spectral envelope from several past spectra, using a predictor codebook. The residual spectrum is coded by a residual codebook. The system operates in the log-spectral domain using a sampled version of the spectral envelope. Experimental results indicate a prediction gain in the range of 9 to 13 dB and an average log-spectral distance of 1.3 to 1.7 dB. Informal listening tests suggest that replacing the conventional scalar quantizer in a 4.8 Kbits/s CELP coder by a VPQ system allows a reduction of the rate assigned to the LPC data from 1.8 Kbits/s to 1.0 Kbits/s without any obvious difference in the perceptual quality.

关键词： Quantization Speech coding Vectors Speech analysis linear predictive coding Delay Switches Signal processing System testing Nonlinear filters

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Transform predictive coding of wideband speech signals

Transform predictive coding of wideband speech signals

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Juin-Hwey Chen Dongmei Wang Speech Coding Research Department AT and T Bell Laboratories Inc. Murray Hill NJ USA AT&T Bell Laboratories Georgia Institute of Technology Atlanta GA USA

ISBN: (纸本)0780331923

This paper presents a novel wideband speech coding algorithm called transform predictive coding (TPC). The main emphasis is on low complexity. TPC uses short-term and long-term prediction to remove the redundancy in speech. The prediction residual is quantized in the frequency domain based on a calculated noise masking threshold. In its simplest form, the TPC coder uses only open-loop quantization and therefore has a low complexity. A 16 kb/s full-duplex, open-loop TPC coder takes only 22% of the CPU load on a 150 MHz SGI Indy workstation and about 34% on a 90 MHz Pentium PC. The speech quality of TPC is almost transparent at 32 kb/s, very good at 24 kb/s, and acceptable at 16 kb/s. In the second half of the paper, we report our recent progress in using closed-loop quantization techniques to improve TPC output speech quality.

关键词： predictive coding Wideband Speech coding linear predictive coding Quantization Sampling methods Narrowband Workstations Speech processing Application software

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