Parallel Model Combination Method Research Articles

The integration of principal component analysis and cepstral mean subtraction in parallel model combination for robust speech recognition

This paper addresses the problem of automatic speech recognition in real applications in which the speech signal is altered by various noises. Feature compensation and model compensation robustness methods are studied. Parallel model combination (PMC) and its recent advances are reviewed and a novel algorithm called PC-PMC is proposed. This algorithm utilizes cepstral mean subtraction (CMS) normalization ability and principal component analysis (PCA) compression and de-correlation capability in the combination with PMC model transformation method. PC-PMC algorithm takes the advantages of additive noise compensation ability of PMC and convolutional noise removal capability of CMS and PCA. In realizing PC-PMC mainly two problems should be solved. The first problem is that PMC method requires invertible modules in the front-end of the system while CMS normalization is not an invertible process. Moreover, when the recognition system is exposed to noisy speech, the adaptation of the PCA transform is required; therefore a framework is to be designed for adaptation of the PCA transform in the presence of noise. The method presented in this paper provides solutions to these problems. Our evaluations are done on the four different real noisy tasks using Nevisa HMM-based, Persian continuous speech recognition system. Experimental results demonstrate significant reduction in the system word error rate using PC-PMC. In addition, the effects of covariance matrix compensation, dynamic features adaptation and the effect of gain parameter in the PMC method are also studied and experimented in the real acoustic conditions. Besides, we have investigated the using of maximum likelihood linear regression (MLLR) and maximum a posteriori (MAP) adaptation techniques in the combination with the introduced PC-PMC method. Finally, a comprehensive discussion on the capabilities of the studied robustness methods is presented.

Speech recognition in a noisy environment using a speech signal estimation method based on the Kalman filter

AbstractThis paper proposes a speech signal estimation method based on the Kalman filter, as preprocessing for speech recognition in a noisy environment. Hitherto, the Kalman filter has been considered unsuited to real‐time processing, since it requires a tremendous amount of computation. Consequently, the purpose of this paper is to reduce the amount of computation in the Kalman filter and to propose a speech signal estimation method for real‐time processing, using high‐speed operation. In order to evaluate the proposed method, a word recognition experiment was performed, using a speech signal extracted from speed with superposed noise. The accuracy of the word recognition tests is compared to the conventional spectral subtraction method and the parallel model combination method in order to demonstrate that the proposed method can deal automatically with various kinds of stationary noise without manual adjustment of the filter parameters for the conditions, such as the speaker, the kind of noise, and the SNR. For this purpose, the range of noise compensation by the proposed method is investigated. It is verified that the proposed method achieves a high word recognition rate, even in the presence of noise that degraded the recognition rate in the conventional method. In particular, the proposed method is effective in environments with a low SNR. © 2004 Wiley Periodicals, Inc. Syst Comp Jpn, 35(3): 46–57, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10257

Adaptation method using expectation-maximisation for noisy speech recognition

An expectation-maximisation-based adaptation method for the mean of noise is proposed for noisy speech recognition. The noisy testing speech is used for the unsupervised adaptation by employing the parallel model combination (PMC). The proposed method has shown improved performances compared with the PMC method in the isolated word recognition experiments.

New approaches for domain transformation and parameter combination for improved accuracy in parallel model combination (PMC) techniques

Parallel model combination (PMC) techniques have been very successful and popularly used in many applications to improve the performance of speech recognition systems under noisy environments. However, it is believed that some assumptions and approximations made in this approach, primarily in the domain transformation and parameter combination processes, are not necessarily accurate enough in certain practical situations, which may degrade the achievable performance of PMC. In this paper, the possible sources that cause the performance degradation in these processes are carefully analyzed and discussed. Three new approaches, including the truncated Gaussian approach and the split mixture approach for the domain transformation process and the estimated cross-term approach for parameter combination process, are proposed in this paper in order to handle these problems, minimize such degradation, and improve the accuracy of the PMC techniques. These proposed approaches were analyzed and discussed with two recognition tasks, one relatively simple, and the other more complicated and realistic. Both sets of experiments showed that these proposed approaches are able to provide significant improvements over the original PMC method, especially when the SNR condition is worse.

A fast algorithm for parallel model combination for noisy speech recognition

Based on the log-normal assumption, parallel model combination (PMC) provides an effective method to adapt the cepstral means and variances of speech models for noisy speech recognition. In addition, the log-add method has been derived to adapt the mean by ignoring the cepstral variance during the process of PMC. This method is efficient for speech recognition in a high signal-to-noise ratio (SNR) environment. In this paper, a new interpretation of the log-add method is proposed. This leads to a modified scheme for performing the adaptation procedure in PMC. This modified method is shown to be efficient in improving recognition accuracy in low SNR. Based on this modified PMC method, we derive a direct adaptation procedure for the variance of speech models in the cepstral domain. The proposed method is a fast algorithm because the computation for the transformation of the covariance matrix is no longer required. Three recognition tasks are conducted to evaluate the proposed method. Experimental results show that the proposed technique not only requires lower computational cost but it also outperforms the original PMC technique in noisy environments.

Cepstral parameter compensation for HMM recognition in noise

This paper describes a method of adapting a continuous density HMM recogniser trained on clean cepstral speech data to make it robust to noise. The technique is based on parallel model combination (PMC) in which the parameters of corresponding pairs of speech and noise states are combined to yield a set of compensated parameters. It improves on earlier cepstral mean compensation methods in that it also adapts the variances and as a result can deal with much lower SNRs. The PMC method is evaluated on the NOISEX-92 noise database and shown to work well down to 0 dB SNR and below for both stationary and non-stationary noises. Furthermore, for relatively constant noise conditions, there is no additional computational cost at run-time.