A Bayesian Network View on Acoustic Model-Based Techniques for Robust Speech Recognition

This article provides a unifying Bayesian network view on various approaches for acoustic model adaptation, missing feature, and uncertainty decoding that are well-known in the literature of robust automatic speech recognition. The representatives of these classes can often be deduced from a Bayesian network that extends the conventional hidden Markov models used in speech recognition. These extensions, in turn, can in many cases be motivated from an underlying observation model that relates clean and distorted feature vectors. By converting the observation models into a Bayesian network representation, we formulate the corresponding compensation rules leading to a unified view on known derivations as well as to new formulations for certain approaches. The generic Bayesian perspective provided in this contribution thus highlights structural differences and similarities between the analyzed approaches

On Separating Environmental and Speaker Adaptation

This paper presents a maximum likelihood (ML) approach, concerned to the background model estimation, in noisy acoustic non-stationary environments. The external noise source is characterised by a time constant convolutional and a time varying additive components. The HMM composition technique, provides a mechanism for integrating parametric models of acoustic background with the signal model, so that noise compensation is tightly coupled with the background model estimation. However, the existing continuous adaptation algorithms usually do not take advantage of this approach, being essentially based on the MLLR algorithm. Consequently, a model for environmental mismatch is not available and, even under constrained conditions a significant number of model parameters have to be updated. From a theoretical point of view only the noise model parameters need to be updated, being the clean speech ones unchanged by the environment. So, it can be advantageous to have a model for environmental mismatch. Additionally separating the additive and convolutional components means a separation between the environmental mismatch and speaker mismatch when the channel does not change for long periods. This approach was followed in the development of the algorithm proposed in this paper. One drawback sometimes attributed to the continuous adaptation approach is that recognition failures originate poor background estimates. This paper also proposes a MAP-like method to deal with this situation

Kalman tracking of linear predictor and harmonic noise models for noisy speech enhancement

This paper presents a speech enhancement method based on the tracking and denoising of the formants of a linear prediction (LP) model of the spectral envelope of speech and the parameters of a harmonic noise model (HNM) of its excitation. The main advantages of tracking and denoising the prominent energy contours of speech are the efficient use of the spectral and temporal structures of successive speech frames and a mitigation of processing artefact known as the ‘musical noise’ or ‘musical tones’.The formant-tracking linear prediction (FTLP) model estimation consists of three stages: (a) speech pre-cleaning based on a spectral amplitude estimation, (b) formant-tracking across successive speech frames using the Viterbi method, and (c) Kalman filtering of the formant trajectories across successive speech frames.The HNM parameters for the excitation signal comprise; voiced/unvoiced decision, the fundamental frequency, the harmonics’ amplitudes and the variance of the noise component of excitation. A frequency-domain pitch extraction method is proposed that searches for the peak signal to noise ratios (SNRs) at the harmonics. For each speech frame several pitch candidates are calculated. An estimate of the pitch trajectory across successive frames is obtained using a Viterbi decoder. The trajectories of the noisy excitation harmonics across successive speech frames are modeled and denoised using Kalman filters.The proposed method is used to deconstruct noisy speech, de-noise its model parameters and then reconstitute speech from its cleaned parts. Experimental evaluations show the performance gains of the formant tracking, pitch extraction and noise reduction stages

HMM modeling of additive noise in the western languages context

This paper is concerned to the noisy speech HMM modelling when the noise is additive, speech independent and the spectral analysis is based on sub-bands. The internal distributions of the noisy speech HMM’s were derived when Gaussian mixture density distributions for clean speech HMM modelling are used, and the noise is normally distributed and additive in the time domain. In these circumstances it is showed that the HMM noisy speech distributions are not Gaussians, however, fitting these distributions as a Gaussian mixture, only a little bit of loss in performance was obtained at very low signal to noise ratios, when compared with the case where the real distributions were computed using Monte Carlo methods

Studies on noise robust automatic speech recognition

Noise in everyday acoustic environments such as cars, traffic environments, and cafeterias remains one of the main challenges in automatic speech recognition (ASR). As a research theme, it has received wide attention in conferences and scientific journals focused on speech technology. This article collection reviews both the classic and novel approaches suggested for noise robust ASR. The articles are literature reviews written for the spring 2009 seminar course on noise robust automatic speech recognition (course code T-61.6060) held at TKK

Likelihood-Maximizing-Based Multiband Spectral Subtraction for Robust Speech Recognition

Automatic speech recognition performance degrades significantly when speech is affected by environmental noise. Nowadays, the major challenge is to achieve good robustness in adverse noisy conditions so that automatic speech recognizers can be used in real situations. Spectral subtraction (SS) is a well-known and effective approach; it was originally designed for improving the quality of speech signal judged by human listeners. SS techniques usually improve the quality and intelligibility of speech signal while speech recognition systems need compensation techniques to reduce mismatch between noisy speech features and clean trained acoustic model. Nevertheless, correlation can be expected between speech quality improvement and the increase in recognition accuracy. This paper proposes a novel approach for solving this problem by considering SS and the speech recognizer not as two independent entities cascaded together, but rather as two interconnected components of a single system, sharing the common goal of improved speech recognition accuracy. This will incorporate important information of the statistical models of the recognition engine as a feedback for tuning SS parameters. By using this architecture, we overcome the drawbacks of previously proposed methods and achieve better recognition accuracy. Experimental evaluations show that the proposed method can achieve significant improvement of recognition rates across a wide range of signal to noise ratios