Speech recognition in noisy environments: a survey

New waves of consumer-centric applications, such as voice search and voice interaction with mobile devices and home entertainment systems, increasingly require automatic speech recognition (ASR) to be robust to the full range of real-world noise and other acoustic distorting conditions. Despite its practical importance, however, the inherent links between and distinctions among the myriad of methods for noise-robust ASR have yet to be carefully studied in order to advance the field further. To this end, it is critical to establish a solid, consistent, and common mathematical foundation for noise-robust ASR, which is lacking at present. This article is intended to fill this gap and to provide a thorough overview of modern noise-robust techniques for ASR developed over the past 30 years. We emphasize methods that are proven to be successful and that are likely to sustain or expand their future applicability. We distill key insights from our comprehensive overview in this field and take a fresh look at a few old problems, which nevertheless are still highly relevant today. Specifically, we have analyzed and categorized a wide range of noise-robust techniques using five different criteria: 1) feature-domain vs. model-domain processing, 2) the use of prior knowledge about the acoustic environment distortion, 3) the use of explicit environment-distortion models, 4) deterministic vs. uncertainty processing, and 5) the use of acoustic models trained jointly with the same feature enhancement or model adaptation process used in the testing stage. With this taxonomy-oriented review, we equip the reader with the insight to choose among techniques and with the awareness of the performance-complexity tradeoffs. The pros and cons of using different noise-robust ASR techniques in practical application scenarios are provided as a guide to interested practitioners. The current challenges and future research directions in this field is also carefully analyzed.

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An overview of noise-robust automatic speech recognition

A spoken language system combines speech recognition, natural language processing and human interface technology. It functions by recognizing the person's words, interpreting the sequence of words to obtain a meaning in terms of the application, and providing an appropriate response back to the user. Potential applications of spoken language systems range from simple tasks, such as retrieving information from an existing database (traffic reports, airline schedules), to interactive problem solving tasks involving complex planning and reasoning (travel planning, traffic routing), to support for multilingual interactions. We examine eight key areas in which basic research is needed to produce spoken language systems: (1) robust speech recognition; (2) automatic training and adaptation; (3) spontaneous speech; (4) dialogue models; (5) natural language response generation; (6) speech synthesis and speech generation; (7) multilingual systems; and (8) interactive multimodal systems. In each area, we identify key research challenges, the infrastructure needed to support research, and the expected benefits. We conclude by reviewing the need for multidisciplinary research, for development of shared corpora and related resources, for computational support and far rapid communication among researchers. The successful development of this technology will increase accessibility of computers to a wide range of users, will facilitate multinational communication and trade, and will create new research specialties and jobs in this rapidly expanding area. >

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The challenge of spoken language systems: Research directions for the nineties

According to the invention, speech recognition can be limited to a smaller number making use of commands (16, 17) given by a user. By means of a method and device, according to the invention, it is also possible to activate a speech recognition device by making use, in the activation, of an existing keyboard or a touch-sensitive screen of the device. A method, according to the invention, provides the user with a logical way to activate the speech recognition device in the device simultaneously providing an improved capacity of the speech recognition device. According to the invention, it is also possible to carry out the speech recognition process by making use of commands given by the user irrespective of how the speech recognition device itself is activated. According to an embodiment of the invention, the device comprises a touch-sensitive screen or surface, in which case the information about a single letter or several letters written on the screen is transmitted to the speech recognition device, whereupon speech recognition is limited to words, wherein the letters in question occur. Speech recognition is most preferably limited to a name beginning with the letter written on the touch screen by the user.

Method and a device for recognizing speech

Root cepstral analysis: a unified view: application to speech processing in car noise environments

The acoustic mismatch between testing and training conditions is known to severely degrade the performance of speech recognition systems. Segmental feature vector normalization was found to improve the noise robustness of mel-frequency cepstral coefficients (MFCC) feature vectors and to outperform other state-of-the-art noise compensation techniques in speaker-dependent recognition. The objective of feature vector normalization is to provide environment-independent parameter statistics in all noise conditions. We propose a more efficient implementation approach for feature vector normalization where the normalization coefficients are computed in a recursive way. Speaker-dependent recognition experiments show that the recursive normalization approach obtains over 60%, the segmental method approximately 50%, and parallel model combination a 14% overall error rate reduction, respectively. Moreover, in the recursive case, this performance gain is obtained with the smallest implementation costs. Also in speaker-independent connected digit recognition, over a 16% error rate reduction is obtained with the proposed feature vector normalization approach.

A recursive feature vector normalization approach for robust speech recognition in noise

Three methods of improving speech recognition in noise are considered: energy thresholding, a noise-robust spectral representation called IMELDA, and a set of noise-robust spectral distortion measures. The spectral distortion measures can be seen as normalizing the contrast in the spectrum, a problem which can be transferred to the representation itself, making it computationally more efficient. In speaker-independent alphabet recognition tests in added steady white noise at various levels, IMELDA is shown to outperform a weighted cepstrum representation and be computationally more efficient. With this material and with digits recorded in trucks at a wide range of noise levels, performance is found to depend strongly on the threshold level. Contrast normalization is found to help, but only when the energy threshold is far from its optimum level. >

D.K. Bye

Papers

A recursive feature vector normalization approach for robust speech recognition in noise

Spectral contrast normalization and other techniques for speech recognition in noise