Speech recognition in noisy environments: a survey

The problem of noise reduction has attracted a considerable amount of research attention over the past several decades. Among the numerous techniques that were developed, the optimal Wiener filter can be considered as one of the most fundamental noise reduction approaches, which has been delineated in different forms and adopted in various applications. Although it is not a secret that the Wiener filter may cause some detrimental effects to the speech signal (appreciable or even significant degradation in quality or intelligibility), few efforts have been reported to show the inherent relationship between noise reduction and speech distortion. By defining a speech-distortion index to measure the degree to which the speech signal is deformed and two noise-reduction factors to quantify the amount of noise being attenuated, this paper studies the quantitative performance behavior of the Wiener filter in the context of noise reduction. We show that in the single-channel case the a posteriori signal-to-noise ratio (SNR) (defined after the Wiener filter) is greater than or equal to the a priori SNR (defined before the Wiener filter), indicating that the Wiener filter is always able to achieve noise reduction. However, the amount of noise reduction is in general proportional to the amount of speech degradation. This may seem discouraging as we always expect an algorithm to have maximal noise reduction without much speech distortion. Fortunately, we show that speech distortion can be better managed in three different ways. If we have some a priori knowledge (such as the linear prediction coefficients) of the clean speech signal, this a priori knowledge can be exploited to achieve noise reduction while maintaining a low level of speech distortion. When no a priori knowledge is available, we can still achieve a better control of noise reduction and speech distortion by properly manipulating the Wiener filter, resulting in a suboptimal Wiener filter. In case that we have multiple microphone sensors, the multiple observations of the speech signal can be used to reduce noise with less or even no speech distortion

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New insights into the noise reduction Wiener filter

inability to deal with signals in the high -freq uency range. Psychoacoustic evidence sugge sts that different perceptual mechanisms are involved to handle resolved and unresolved hannonics. We propose a system for speech segre gation that deals with low-frequency and high-frequency signals differently. For low-frequency signals , our model generates segm ents based on temporal continuity and cross-channel correlation , and groups them according to periodicity. For high-frequency signals, the model generates segments based on common amplitude modulation (AM) in addition to temporal contin uity, and grou ps them according to AM repetition rates. Underlying the group ing process is a p itch contour that is first est imated from segregated speech based on global pitch and then verified by psychoacoustic constraints. Our system is systematically evaluated, and it yields substantially better performan ce than previous CASA systems, especially in the high-frequency range.

Monaural speech segregation based on pitch tracking and

Reducing the interference noise in a monaural noisy speech signal has been a challenging task for many years. Compared to traditional unsupervised speech enhancement methods, e.g., Wiener filtering, supervised approaches, such as algorithms based on hidden Markov models (HMM), lead to higher-quality enhanced speech signals. However, the main practical difficulty of these approaches is that for each noise type a model is required to be trained a priori. In this paper, we investigate a new class of supervised speech denoising algorithms using nonnegative matrix factorization (NMF). We propose a novel speech enhancement method that is based on a Bayesian formulation of NMF (BNMF). To circumvent the mismatch problem between the training and testing stages, we propose two solutions. First, we use an HMM in combination with BNMF (BNMF-HMM) to derive a minimum mean square error (MMSE) estimator for the speech signal with no information about the underlying noise type. Second, we suggest a scheme to learn the required noise BNMF model online, which is then used to develop an unsupervised speech enhancement system. Extensive experiments are carried out to investigate the performance of the proposed methods under different conditions. Moreover, we compare the performance of the developed algorithms with state-of-the-art speech enhancement schemes using various objective measures. Our simulations show that the proposed BNMF-based methods outperform the competing algorithms substantially.

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Supervised and Unsupervised Speech Enhancement Using Nonnegative Matrix Factorization

Segregating speech from one monaural recording has proven to be very challenging. Monaural segregation of voiced speech has been studied in previous systems that incorporate auditory scene analysis principles. A major problem for these systems is their inability to deal with the high-frequency part of speech. Psychoacoustic evidence suggests that different perceptual mechanisms are involved in handling resolved and unresolved harmonics. We propose a novel system for voiced speech segregation that segregates resolved and unresolved harmonics differently. For resolved harmonics, the system generates segments based on temporal continuity and cross-channel correlation, and groups them according to their periodicities. For unresolved harmonics, it generates segments based on common amplitude modulation (AM) in addition to temporal continuity and groups them according to AM rates. Underlying the segregation process is a pitch contour that is first estimated from speech segregated according to dominant pitch and then adjusted according to psychoacoustic constraints. Our system is systematically evaluated and compared with pervious systems, and it yields substantially better performance, especially for the high-frequency part of speech.

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Monaural speech segregation based on pitch tracking and amplitude modulation

An improved hidden Markov model-based (HMM-based) speech enhancement system designed using the minimum mean square error principle is implemented and compared with a conventional spectral subtraction system. The improvements to the system are: (1) incorporation of mixture components in the HMM for noise in order to handle noise nonstationarity in a more flexible manner, (2) two efficient methods in the speech enhancement system design that make the system real-time implementable, and (3) an adaptation method to the noise type in order to accommodate a wide variety of noise expected under the enhancement system's operating environment. The results of the experiments designed to evaluate the performance of the HMM-based speech enhancement systems in comparison with spectral subtraction are reported. Three types of noise-white noise, simulated helicopter noise, and multitalker (cocktail party) noise-were used to corrupt the test speech signals. Both objective (global SNR) and subjective mean opinion score (MOS) evaluations demonstrate consistent superiority of the HMM-based enhancement systems that incorporate the innovations described in this paper over the conventional spectral subtraction method.

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HMM-based strategies for enhancement of speech signals embedded in nonstationary noise

We have investigated effectiveness of several HMM-based speech enhancement strategies in the hearing aid context and compared their performance to a traditional method based on spectral subtraction (also called Wiener filtering). The performance of the various systems has been evaluated using several types of noise likely to be encountered by hearing impaired listeners. Both the objective and subjective evaluations demonstrate consistent superiority of the HMM-based enhancement methods over the spectral subtraction method. The superiority is more evident for sentences contaminated by nonstationary noises at low input SNRs. >

Comparative performance of spectral subtraction and HMM-based speech enhancement strategies with application to hearing and design

We describe our recent work on the real-time implementation of a state-of-the-art HMM-based MMSE speech enhancement algorithm, where our earlier published algorithm has been approximated, optimized, and simplified The key innovations enabling the enhancement system to run in real-time are: (1) an algorithm for automatic selection of the noise model, (2) pruning the MMSE forward calculations, (3) a new pause detection method useable for a SNR down to 0 dB, and (4) task partitioning of the entire system A preliminary version of the real-time enhancement system is simulated on the IBM-PC 486DX2/66 and at the same time implemented on a DSP platform based on dual TMS320C30 DSP chips using single precision floating point arithmetic

R.L. Brennan

Papers

HMM-based strategies for enhancement of speech signals embedded in nonstationary noise

Comparative performance of spectral subtraction and HMM-based speech enhancement strategies with application to hearing and design

Real-time implementation of HMM-based MMSE algorithm for speech enhancement in hearing aid applications