Speaker recognition using frequency filtered spectral energies

The spectral parameters that result from filtering the frequency sequence of log mel-scaled filter-bank energies with a simple first or second order FIR filter have proved to be an efficient speech representation in terms of both speech recognition rate and computational load. Recently, the authors have shown that this frequency filtering can approximately equalize the cepstrum variance enhancing the oscillations of the spectral envelope curve that are most effective for discrimination between speakers. Even better speaker identification results than using melcepstrum have been obtained on the TIMIT database, especially when white noise was added. On the other hand, the hybridization of both linear prediction and filter-bank spectral analysis using either cepstral transformation or the alternative frequency filtering has been explored for speaker verification. The combination of hybrid spectral analysis and frequency filtering, that had shown to be able to outperform the conventional techniques in clean and noisy word recognition, has yield good text-dependent speaker verification results on the new speaker-oriented telephone-line POLYCOST database.Peer ReviewedPostprint (published version

Linear prediction of the one-sided autocorrelation sequence for noisy speech recognition

The article presents a robust representation of speech based on AR modeling of the causal part of the autocorrelation sequence. In noisy speech recognition, this new representation achieves better results than several other related techniques.Peer ReviewedPostprint (published version

Text-Independent, Open-Set Speaker Recognition

Speaker recognition, like other biometric personal identification techniques, depends upon a person\u27s intrinsic characteristics. A realistically viable system must be capable of dealing with the open-set task. This effort attacks the open-set task, identifying the best features to use, and proposes the use of a fuzzy classifier followed by hypothesis testing as a model for text-independent, open-set speaker recognition. Using the TIMIT corpus and Rome Laboratory\u27s GREENFLAG tactical communications corpus, this thesis demonstrates that the proposed system succeeded in open-set speaker recognition. Considering the fact that extremely short utterances were used to train the system (compared to other closed-set speaker identification work), this system attained reasonable open-set classification error rates as low as 23% for TIMIT and 26% for GREENFLAG. Feature analysis identified the filtered linear prediction cepstral coefficients with or without the normalized log energy or pitch appended as a robust feature set (based on the 17 feature sets considered), well suited for clean speech and speech degraded by tactical communications channels

Band-pass filtering of the time sequences of spectral parameters for robust wireless speech recognition

In this paper we address the problem of automatic speech recognition when wireless speech communication systems are involved. In this context, three main sources of distortion should be considered: acoustic environment, speech coding and transmission errors. Whilst the first one has already received a lot of attention, the last two deserve further investigation in our opinion. We have found out that band-pass filtering of the recognition features improves ASR performance when distortions due to these particular communication systems are present. Furthermore, we have evaluated two alternative configurations at different bit error rates (BER) typical of these channels: band-pass filtering the LP-MFCC parameters or a modification of the RASTA-PLP using a sharper low-pass section perform consistently better than LP-MFCC and RASTA-PLP, respectively.Publicad

On the decorrelation of filter-bank energies in speech recognition

Cepstral coefficients are widely used in speech recognition. In this paper, we claim that they are not the best way of representing the spectral envelope, at least for some usual speech recognition systems. In fact, cepstrum has several disadvantages: poor physical meaning, need of transformation, and low capacity of adaptation to some recognition systems. In this paper, we propose a new representation that significantly outperforms both mel-cepstrum and LPC-cepstrum techniques in both recognition rate and computational cost. It consists of filtering the frequency sequence of filter-bank energies with an extremely simple filter that equalizes the variance of the cepstral coefficients. Excellent results of the new technique using a continuous observation density HMM recognition system and two very different recognition tasks, connected digits and phone recognition, are presented.Peer ReviewedPostprint (published version

A comparison of features for large population speaker identification

Bibliography: leaves 95-104.Speech recognition systems all have one criterion in common; they perform better in a controlled environment using clean speech. Though performance can be excellent, even exceeding human capabilities for clean speech, systems fail when presented with speech data from more realistic environments such as telephone channels. The differences using a recognizer in clean and noisy environments are extreme, and this causes one of the major obstacles in producing commercial recognition systems to be used in normal environments. It is the lack of performance of speaker recognition systems with telephone channels that this work addresses. The human auditory system is a speech recognizer with excellent performance, especially in noisy environments. Since humans perform well at ignoring noise more than any machine, auditory-based methods are the promising approaches since they attempt to model the working of the human auditory system. These methods have been shown to outperform more conventional signal processing schemes for speech recognition, speech coding, word-recognition and phone classification tasks. Since speaker identification has received lot of attention in speech processing because of its waiting real-world applications, it is attractive to evaluate the performance using auditory models as features. Firstly, this study rums at improving the results for speaker identification. The improvements were made through the use of parameterized feature-sets together with the application of cepstral mean removal for channel equalization. The study is further extended to compare an auditory-based model, the Ensemble Interval Histogram, with mel-scale features, which was shown to perform almost error-free in clean speech. The previous studies of Elli to be more robust to noise were conducted on speaker dependent, small population, isolated words and now are extended to speaker independent, larger population, continuous speech. This study investigates whether the Elli representation is more resistant to telephone noise than mel-cepstrum as was shown in the previous studies, when now for the first time, it is applied for speaker identification task using the state-of-the-art Gaussian mixture model system

Recognition of numbers by using demisyllables and hidden Markov models

Peer ReviewedPostprint (published version

Applications of missing feature theory to speaker recognition

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.Includes bibliographical references (p. 100-101).An important problem in speaker recognition is the degradation that occurs when speaker models trained with speech from one type of channel are used to score speech from another type of channel, known as channel mismatch. This thesis investigates various channel compensation techniques and approaches from missing feature theory for improving Gaussian mixture model (GMM)-based speaker verification under this mismatch condition. Experiments are performed using a speech corpus consisting of "clean" training speech and "dirty" test speech equal to the clean speech corrupted by additive Gaussian noise. Channel compensation methods studied are cepstral mean subtraction, RASTA, and spectral subtraction. Approaches to missing feature theory include missing feature compensation, which removes corrupted features, and missing feature restoration which predicts such features from neighboring features in both frequency and time. These methods are investigated both individually and in combination. In particular, missing feature compensation combined with spectral subtraction in the discrete Fourier transform domain significantly improves GMM speaker verification accuracy and outperforms all other methods examined in this thesis, reducing the equal error rate by about 10% more than other methods over a SNR range of 5-25 dB. Moreover, this considerably outperforms a state-of-the-art GMM recognizer for the mismatch application that combines missing feature theory with spectral subtraction developed in a mel-filter energy domain. Finally, the concept of missing restoration is explored. A novel linear minimum mean-squared-error missing feature estimator is derived and applied to pure vowels as well as a clean/dirty verification trial. While it does not improve performance in the verification trial, a large SNR improvement for features estimated for the pure vowel case indicate promise in the application of this method.by Michael Thomas Padilla.S.M