Minimum Mean-Squared Error Estimation of Mel-Frequency Cepstral Coefficients Using a Novel Distortion Model

In this paper, a new method for statistical estimation of Mel-frequency cepstral coefficients (MFCCs) in noisy speech signals is proposed. Previous research has shown that model-based feature domain enhancement of speech signals for use in robust speech recognition can improve recognition accuracy significantly. These methods, which typically work in the log spectral or cepstral domain, must face the high complexity of distortion models caused by the nonlinear interaction of speech and noise in these domains. In this paper, an additive cepstral distortion model (ACDM) is developed, and used with a minimum mean-squared error (MMSE) estimator for recovery of MFCC features corrupted by additive noise. The proposed ACDM-MMSE estimation algorithm is evaluated on the Aurora2 database, and is shown to provide significant improvement in word recognition accuracy over the baseline

Time-Domain Isolated Phoneme Classification Using Reconstructed Phase Spaces

This paper introduces a novel time-domain approach to modeling and classifying speech phoneme waveforms. The approach is based on statistical models of reconstructed phase spaces, which offer significant theoretical benefits as representations that are known to be topologically equivalent to the state dynamics of the underlying production system. The lag and dimension parameters of the reconstruction process for speech are examined in detail, comparing common estimation heuristics for these parameters with corresponding maximum likelihood recognition accuracy over the TIMIT data set. Overall accuracies are compared with a Mel-frequency cepstral baseline system across five different phonetic classes within TIMIT, and a composite classifier using both cepstral and phase space features is developed. Results indicate that although the accuracy of the phase space approach by itself is still currently below that of baseline cepstral methods, a combined approach is capable of increasing speaker independent phoneme accuracy

Sub-Banded Reconstructed Phase Spaces for Speech Recognition

A novel method combining filter banks and reconstructed phase spaces is proposed for the modeling and classification of speech. Reconstructed phase spaces, which are based on dynamical systems theory, have advantages over spectral-based analysis methods in that they can capture nonlinear or higher-order statistics. Recent work has shown that the natural measure of a reconstructed phase space can be used for modeling and classification of phonemes. In this work, sub-banding of speech, which has been examined for recognition of noise-corrupted speech, is studied in combination with phase space reconstruction. This sub-banding, which is motivated by empirical psychoacoustical studies, is shown to dramatically improve the phoneme classification accuracy of reconstructed phase space-based approaches. Experiments that examine the performance of fused sub-banded reconstructed phase spaces for phoneme classification are presented. Comparisons against a cepstral-based classifier show that the proposed approach is competitive with state-of-the-art methods for modeling and classification of phonemes. Combination of cepstral-based features and the sub-band RPS features shows improvement over a cepstral-only baseline

Estimation of cepstral coefficients for robust speech recognition

This dissertation introduces a new approach to estimation of the features used in an automatic speech recognition system operating in noisy environments, namely mel-frequency cepstral coefficients. A major challenge in the development of an estimator for these features is the nonlinear interaction between a speech signal and the corrupting ambient noise. Previous estimation methods have attempted to deal with this issue with the use of a low order Taylor series expansion, which results in a rough approximation of the true distortion interaction between the speech and noise signal components, and the estimators must typically be iterative, as it is the speech features themselves that are used as expansion points. The new estimation approach, named the additive cepstral distortion model minimum mean-square error estimator, uses a novel distortion model to avoid the necessity of a Taylor series expansion, allowing for a direct solution. Like many previous approaches, the estimator introduced in this dissertation uses a prior distribution model of the speech features. In previous work, this distribution is limited in specificity, as a single global model is trained over an entire set of speech data. The estimation approach introduced in this work extends this method to incorporate contextual information into the prior model, leading to a more specific distribution and subsequently better estimates of the features. An introduction to automatic speech recognition is presented, and a historical review of relevant feature estimation research is given. The new estimation approach is developed, along with a method for implementing more specific prior distribution modeling, and the new feature estimation approach is evaluated on two standard robust speech recognition datasets

THIRD-ORDER MOMENTS OF FILTERED SPEECH SIGNALS FOR ROBUST SPEECH RECOGNITION

Novel speech features calculated from third-order statistics of subband-filtered speech signals are introduced and studied for robust speech recognition. These features have the potential to capture nonlinear information not represented by cepstral coefficients. Also, because the features presented in this paper are based on the third-order moments, they may be more immune to Gaussian noise than cepstrals, as Gaussian distributions have zero third-order moments. Preliminary experiments on the AURORA2 database studying these features in combination with Mel-frequency cepstral coefficients (MFCC’s) are presented, and improvement over the MFCC-only baseline is shown with the combined feature set for several noise conditions. 1

A Comparison of Reconstructed Phase Spaces and Cepstral Coefficients for Multi-Band Phoneme Classification

This paper examines the use of multi-band reconstructed phase spaces as models for phoneme classification. Sub-banding reconstructed phase spaces combines linear, frequency-based techniques with a nonlinear modeling approach to speech recognition. Experiments comparing the effects of filtering speech signals for both reconstructed phase space and traditional speech recognition approaches are presented. These experiments study the use of two non-overlapping subbands for isolated phoneme classification on the TIMIT corpus. It is shown that while classification accuracy using MeI frequency cepstral coefficients as features does not improve with sub-banding, the accuracy increases from 36.1% to 42.0% using sub-banded reconstructed phase spaces to model the phonemes

A combined sub-band and reconstructed phase space approach to phoneme classification

Classificatio

A COMPARISON OF RECONSTRUCTED PHASE SPACES AND CEPSTRAL COEFFICIENTS FOR MULTI-BAND PHONEME CLASSIFICATION

Abstract: This paper examines the use of multi-band reconstructed phase spaces as models for phoneme classification. Sub-banding reconstructed phase spaces combines linear, frequency-based techniques with a nonlinear modeling approach to speech recognition. Experiments comparing the effects of filtering speech signals for both reconstructed phase space and traditional speech recognition approaches are presented. These experiments study the use of two non-overlapping subbands for isolated phoneme classification on the TIMIT corpus. It is shown that while classification accuracy using Mel frequency cepstral coefficients as features does not improve with sub-banding, the accuracy increases from 36.1 % to 42.0 % using sub-banded reconstructed phase spaces to model the phonemes. 1