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Experimental evaluation of features for robust speaker identification

October 1, 1994
  |
Journal Article
Author:
Douglas A. Reynolds
Published in:
IEEE Trans. Speech Audio Process., Vol. 2, No. 4, October 1994, pp. 639-643.
Topic:
speaker recognition
R&D area:
R&D group:

Summary

This correspondence presents an experimental evaluation of different features and channel compensation techniques for robust speaker identification. The goal is to keep all processing and classification steps constant and to vary only the features and compensations used to allow a controlled comparison. A general, maximum-likelihood classifier based on Gaussian mixture densities is used as the classifier, and experiments are conducted on the King speech database, a conversational, telephone-speech database. The features examined are mel-frequency and linear-frequency filterbank cepstral coefficients, linear prediction ceptral coefficients. The channel compensation techniques examined are cepstral mean removal, RASTA processing, and a quadratic trend removal technique. It is shown for this database that performance difference between the basic features is small, and the major gains are due to the channel compensation techniques. The best "across-the-divide" recognition accuracy of 92% is obtained for both high-order LPC features and band-limited filterbank features.
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Summary

This correspondence presents an experimental evaluation of different features and channel compensation techniques for robust speaker identification. The goal is to keep all processing and classification steps constant and to vary only the features and compensations used to allow a controlled comparison. A general, maximum-likelihood classifier based on Gaussian mixture...

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Experimental evaluation of features for robust speaker identification

Large population speaker recognition using wideband and telephone speech

July 28, 1994
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Conference Paper
Author:
Douglas A. Reynolds
Published in:
Proc. SPIE, Vol. 2277, Automatic Systems for the Identification and Inspection of Humans, 28-29 July 1994, pp. 111-120.
Topic:
speaker recognition
R&D area:
R&D group:

Summary

The two largest factors affecting automatic speaker identification performance are the size of the population to be distinguished among and the degradations introduced by noisy communication channels (e.g. telephone transmission). To experimentally examine these two factors, this paper presents text-independent speaker identification results for varying speaker population sizes up to 630 speakers for both clean, wideband speech and telephone speech. A system based on Gaussian mixture speaker models is used for speaker identification and experiments are conducted on the TIMIT and NTIMIT databases. The aims of this study are to (1) establish how well text-independent speaker identification can perform under near ideal conditions for very large populations (using the TIMIT database), (2) gauge the performance loss incurred by transmitting the speech over the telephone network (using the NTIMIT database), and (3) examine the validity of current models of telephone degradations commonly used in developing compensation techniques (using the NTIMIT calibration signals). This is believed to be the first speaker identification experiments on the complete 630 speaker TIMIT and NTIMIT databases and the largest text-independent speaker identification task reported to date. Identification accuracies of 99.5% and 60.7% are achieved on the TIMIT and NTIMIT databases, respectively.
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Summary

The two largest factors affecting automatic speaker identification performance are the size of the population to be distinguished among and the degradations introduced by noisy communication channels (e.g. telephone transmission). To experimentally examine these two factors, this paper presents text-independent speaker identification results for varying speaker population sizes up to...

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Large population speaker recognition using wideband and telephone speech

Wordspotter training using figure-of-merit back propagation

April 19, 1994
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Conference Paper
Author:
Richard P. Lippmann
…
Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, Vol. 1, Speech Processing, 19-22 April 1994, pp. 389-392.
Topic:
machine learning
R&D area:
R&D group:

Summary

A new approach to wordspotter training is presented which directly maximizes the Figure of Merit (FOM) defined as the average detection rate over a specified range of false alarm rates. This systematic approach to discriminant training for wordspotters eliminates the necessity of ad hoc thresholds and tuning. It improves the FOM of wordspotters tested using cross-validation on the credit-card speech corpus training conversations by 4 to 5 percentage points to roughly 70% This improved performance requires little extra complexity during wordspotting and only two extra passes through the training data during training. The FOM gradient is computed analytically for each putative hit, back-propagated through HMM word models using the Viterbi alignment, and used to adjust RBF hidden node centers and state-weights associated with every node in HMM keyword models.
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Summary

A new approach to wordspotter training is presented which directly maximizes the Figure of Merit (FOM) defined as the average detection rate over a specified range of false alarm rates. This systematic approach to discriminant training for wordspotters eliminates the necessity of ad hoc thresholds and tuning. It improves the...

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Wordspotter training using figure-of-merit back propagation

Automatic language identification of telephone speech messages using phoneme recognition and N-gram modeling

April 19, 1994
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Conference Paper
Author:
Marc A. Zissman
…
Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, Vol. 1, Speech Processing, 19-22 April 1994, pp. 305-308.
Topic:
language recognition
R&D area:
R&D group:

Summary

This paper compares the performance of four approaches to automatic language identification (LID) of telephone speech messages: Gaussian mixture model classification (GMM), language-independent phoneme recognition followed by language-dependent language modeling (PRLM), parallel PRLM (PRLM-P), and language-dependent parallel phoneme recognition (PPR). These approaches span a wide range of training requirements and levels of recognition complexity. All approaches were tested on the development test subset of the OGI multi-language telephone speech corpus. Generally, system performance was directly related to system complexity, with PRLM-P and PPR performing best. On 45 second test utterance, average two language, closed-set, forced-choice classification performance, reached 94.5% correct. The best 10 language, closed-set, forced-choice performance was 79.2% correct.
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Summary

This paper compares the performance of four approaches to automatic language identification (LID) of telephone speech messages: Gaussian mixture model classification (GMM), language-independent phoneme recognition followed by language-dependent language modeling (PRLM), parallel PRLM (PRLM-P), and language-dependent parallel phoneme recognition (PPR). These approaches span a wide range of training requirements and...

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Automatic language identification of telephone speech messages using phoneme recognition and N-gram modeling

Demonstrations and applications of spoken language technology: highlights and perspectives from the 1993 ARPA Spoken Language Technology and Applications Day

April 19, 1994
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Conference Paper
Author:
Clifford J. Weinstein
Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, Vol. 1, Speech Processing, 19-22 April 1994, pp. 337-340.
Topic:
speech recognition
R&D area:
R&D group:

Summary

The ARPA Spoken Language Technology and Applications Day (SLTA'93) was a special workshop which presented a set of live, state-of-the-art demonstrations of speech recognition and Spoken Language Understanding systems. The purpose of this paper is to provide perspective on current opportunities for applications which they can enable, and reviewing the applications opportunities and needs cited by panelists and other members of the user community.
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Summary

The ARPA Spoken Language Technology and Applications Day (SLTA'93) was a special workshop which presented a set of live, state-of-the-art demonstrations of speech recognition and Spoken Language Understanding systems. The purpose of this paper is to provide perspective on current opportunities for applications which they can enable, and reviewing the...

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Demonstrations and applications of spoken language technology: highlights and perspectives from the 1993 ARPA Spoken Language Technology and Applications Day

Integrated models of signal and background with application to speaker identification in noise

April 1, 1994
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Journal Article
Author:
Richard C. Rose
…
Published in:
IEEE Trans. Speech Audio Process., Vol. 2, No. 2, April 1994, pp. 245-257.
Topic:
speaker recognition
R&D area:
R&D group:

Summary

This paper is concerned with the problem of robust parametric model estimation and classification in noisy acoustic environments. Characterization and modeling of the external noise sources in these environments is in itself an important issue in noise compensation. The techniques described here provide a mechanism for integrating parametric models of acoustic background with the signal model so that noise compensation is tightly coupled with signal model training and classification. Prior information about the acoustic background process is provided using a maximum likelihood parameter estimation procedure that integrates an a priori model of acoustic background with the signal model. An experimental study is presented in the paper on the application of this approach to text-independent speaker identification in noisy acoustic environments. Considerable improvement in speaker classification performance was obtained for classifying unlabeled sections of conversational speech utterances from a 16-speaker population under cross-environment training and testing conditions.
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Summary

This paper is concerned with the problem of robust parametric model estimation and classification in noisy acoustic environments. Characterization and modeling of the external noise sources in these environments is in itself an important issue in noise compensation. The techniques described here provide a mechanism for integrating parametric models of...

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Integrated models of signal and background with application to speaker identification in noise

Digital signal processing applications in cochlear-implant research

March 1, 1994
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Journal Article
Author:
Joseph Tierney
…
Published in:
Lincoln Laboratory Journal, Vol. 7, No. 1, Spring 1994, pp. 31-62.
Topic:
signal processing
R&D area:
R&D group:

Summary

We have developed a facility that enables scientists to investigate a wide range of sound-processing schemes for human subjects with cochlear implants. This digital signal processing (DSP) facility-named the Programmable Interactive System for Cochlear Implant Electrode Stimulation (PISCES)-was designed, built, and tested at Lincoln Laboratory and then installed at the Cochlear Implant Research Laboratory (CIRL) of the Massachusetts Eye and Ear Infirmary (MEEI). New stimulator algorithms that we designed and ran on PISCES have resulted in speech-reception improvements for implant subjects relative to commercial implant stimulators. These improvements were obtained as a result of interactive algorithm adjustment in the clinic, thus demonstrating the importance of a flexible signal-processing facility. Research has continued in the development of a laboratory-based, sohare-controlled, real-time, speech processing system; the exploration of new sound-processing algorithms for improved electrode stimulation; and the design of wearable stimulators that will allow subjects full-time use of stimulator algorithms developed and tested in a laboratory setting.
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Summary

We have developed a facility that enables scientists to investigate a wide range of sound-processing schemes for human subjects with cochlear implants. This digital signal processing (DSP) facility-named the Programmable Interactive System for Cochlear Implant Electrode Stimulation (PISCES)-was designed, built, and tested at Lincoln Laboratory and then installed at the...

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Digital signal processing applications in cochlear-implant research

Neural networks, Bayesian a posteriori probabilities, and pattern classification

January 1, 1994
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Book Chapter
Author:
Richard P. Lippmann
Published in:
Chapter 4 in From Statistics to Neural Networks: Theory and Pattern Recognition Applications, 1994, pp. 83-104.
Topic:
machine learning
R&D area:
R&D group:

Summary

Researchers in the fields of neural networks, statistics, machine learning, and artificial intelligence have followed three basic approaches to developing new pattern classifiers. Probability Density Function (PDF) classifiers include Gaussian and Gaussian Mixture classifiers which estimate distributions or densities of input features separately for each class. Posterior probability classifiers include multilayer perceptron neural networks with sigmoid nonlinearities and radial basis function networks. These classifiers estimate minimum-error Bayesian a posteriori probabilities (hereafter referred to as posterior probabilities) simultaneously for all classes. Boundary forming classifiers include hard-limiting single-layer perceptrons, hypersphere classifiers, and nearest neighbor classifiers. These classifiers have binary indicator outputs which form decision regions that specify the class of any input pattern. Posterior probability and boundary-forming classifiers are trained using discriminant training. All training data is used simultaneously to estimate Bayesian posterior probabilities or minimize overall classification error rates. PDF classifiers are trained using maximum likelihood approaches which individually model class distributions without regard to overall classification performance. Analytic results are presented which demonstrate that many neural network classifiers can accurately estimate posterior probabilities and that these neural network classifiers can sometimes provide lower error rates than PDF classifiers using the same number of trainable parameters. Experiments also demonstrate how interpretation of network outputs as posterior probabilities makes it possible to estimate the confidence of a classification decision, compensate for differences in class prior probabilities between test and training data, and combine outputs of multiple classifiers over time for speech recognition.
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Summary

Researchers in the fields of neural networks, statistics, machine learning, and artificial intelligence have followed three basic approaches to developing new pattern classifiers. Probability Density Function (PDF) classifiers include Gaussian and Gaussian Mixture classifiers which estimate distributions or densities of input features separately for each class. Posterior probability classifiers include...

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Neural networks, Bayesian a posteriori probabilities, and pattern classification

Predicting the risk of complications in coronary artery bypass operations using neural networks

January 1, 1994
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Conference Paper
Author:
Richard P. Lippmann
…
Published in:
Proc. 7th Int. Conf. on Neural Information Processing Systems, NIPS, 1994, pp. 1055-62.
Topic:
machine learning
R&D area:
R&D group:

Summary

Experiments demonstrated that sigmoid multilayer perceptron (MLP) networks provide slightly better risk prediction than conventional logistic regression when used to predict the risk of death, stroke, and renal failure on 1257 patients who underwent coronary artery bypass operations at the Lahey Clinic. MLP networks with no hidden layer and networks with one hidden layer were trained using stochastic gradient descent with early stopping. MLP networks and logistic regression used the same input features and were evaluated using bootstrap sampling with 50 replications. ROC areas for predicting mortality using preoperative input features were 70.5% for logistic regression and 76.0% for MLP networks. Regularization provided by early stopping was an important component of improved performance. A simplified approach to generating confidence intervals for MLP risk predictions using an auxiliary "confidence MLP" was developed. The confidence MLP is trained to reproduce confidence intervals that were generated during training using the outputs of 50 MLP networks trained with different bootstrap samples.
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Summary

Experiments demonstrated that sigmoid multilayer perceptron (MLP) networks provide slightly better risk prediction than conventional logistic regression when used to predict the risk of death, stroke, and renal failure on 1257 patients who underwent coronary artery bypass operations at the Lahey Clinic. MLP networks with no hidden layer and networks...

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Predicting the risk of complications in coronary artery bypass operations using neural networks

Figure of merit training for detection and spotting

November 29, 1993
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Conference Paper
Author:
Eric I. Chang
…
Published in:
Proc. Neural Information Processing Systems, NIPS, 29 November - 2 December 1993.
Topic:
speaker recognition
R&D area:
R&D group:

Summary

Spotting tasks require detection of target patterns from a background of richly varied non-target inputs. The performance measure of interest for these tasks, called the figure of merit (FOM), is the detection rate for target patterns when the false alarm rate is in an acceptable range. A new approach to training spotters is presented which computes the FOM gradient for each input pattern and then directly maximizes the FOM using back propagation. This eliminates the need for thresholds during training. It also uses network resources to model Bayesian a posteriori probability functions accurately only for patterns which have a significant effect on the detection accuracy over the false alarm rate of interest. FOM training increased detection accuracy by 5 percentage points for a hybrid radial basis function (RBF) - hidden Markov model (HMM) wordspotter on the credit-card speech corpus.
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Summary

Spotting tasks require detection of target patterns from a background of richly varied non-target inputs. The performance measure of interest for these tasks, called the figure of merit (FOM), is the detection rate for target patterns when the false alarm rate is in an acceptable range. A new approach to...

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Figure of merit training for detection and spotting
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