Speaker recognition

About: Speaker recognition is a research topic. Over the lifetime, 14990 publications have been published within this topic receiving 310061 citations.

Fast speaker adaptation of hybrid NN/HMM model for speech recognition based on discriminative learning of speaker code

Abstract: In this paper, we propose a new fast speaker adaptation method for the hybrid NN-HMM speech recognition model. The adaptation method depends on a joint learning of a large generic adaptation neural network for all speakers as well as multiple small speaker codes (one per speaker). The joint training method uses all training data along with speaker labels to update adaptation NN weights and speaker codes based on the standard back-propagation algorithm. In this way, the learned adaptation NN is capable of transforming each speaker features into a generic speaker-independent feature space when a small speaker code is given. Adaptation to a new speaker can be simply done by learning a new speaker code using the same back-propagation algorithm without changing any NN weights. In this method, a separate speaker code is learned for each speaker while the large adaptation NN is learned from the whole training set. The main advantage of this method is that the size of speaker codes is very small. As a result, it is possible to conduct a very fast adaptation of the hybrid NN/HMM model for each speaker based on only a small amount of adaptation data (i.e., just a few utterances). Experimental results on TIMIT have shown that it can achieve over 10% relative reduction in phone error rate by using only seven utterances for adaptation.

Advances in channel compensation for SVM speaker recognition

Abstract: Cross-channel degradation is one of the significant challenges facing speaker recognition systems. We study the problem for speaker recognition using support vector machines (SVMs). We perform channel compensation in SVM modeling by removing non-speaker nuisance dimensions in the SVM expansion space via projections. Training to remove these dimensions is accomplished via an eigenvalue problem. The eigenvalue problem attempts to reduce multisession variation for the same speaker, reduce different channel effects, and increase "distance" between different speakers. We apply our methods to a subset of the Switchboard 2 corpus. Experiments show dramatic improvement in performance for the cross-channel case.

Speaker and Session Variability in GMM-Based Speaker Verification

Abstract: We present a corpus-based approach to speaker verification in which maximum-likelihood II criteria are used to train a large-scale generative model of speaker and session variability which we call joint factor analysis. Enrolling a target speaker consists in calculating the posterior distribution of the hidden variables in the factor analysis model and verification tests are conducted using a new type of likelihood II ratio statistic. Using the NIST 1999 and 2000 speaker recognition evaluation data sets, we show that the effectiveness of this approach depends on the availability of a training corpus which is well matched with the evaluation set used for testing. Experiments on the NIST 1999 evaluation set using a mismatched corpus to train factor analysis models did not result in any improvement over standard methods, but we found that, even with this type of mismatch, feature warping performs extremely well in conjunction with the factor analysis model, and this enabled us to obtain very good results (equal error rates of about 6.2%)

Speech recognition apparatus & method having dynamic reference pattern adaptation

Abstract: A speech recognition apparatus having reference pattern adaptation stores a plurality of reference patterns representing speech to be recognized, each stored reference pattern having associated therewith a quality value representing the effectiveness of that pattern for recognizing an incoming speech utterance. The method and apparatus provide user correction actions representing the accuracy of a speech recognition, dynamically, during the recognition of unknown incoming speech utterances and after training of the system. The quality values are updated, during the speech recognition process, for at least a portion of those reference patterns used during the speech recognition process. Reference patterns having low quality values, indicative of either inaccurate representation of the unknown speech or non-use, can be deleted so long as the reference pattern is not needed, for example, where the reference pattern is the last instance of a known word or phrase. Various methods and apparatus are provided for determining when reference patterns can be deleted or added, to the reference memory, and when the scores or values associated with a reference pattern should be increased or decreased to represent the "goodness" of the reference pattern in recognizing speech.

Lighting control using speech recognition

Abstract: A system and method for the control of color-based lighting through voice control or speech recognition as well as a syntax for use with such a system. In this approach, the spoken voice (in any language) can be used to more naturally control effects without having to learn the myriad manipulation required of some complex controller interfaces. A simple control language based upon spoken words consisting of commands and values is constructed and used to provide a common base for lighting and system control.