Digital Processing Of Speech Signals Rabiner Solution Manual Updated 81
Wiley, 1978), and “Analysis, Synthesis and Applications of Speech Waveforms, ” (Prentice-Hall, 1983) with J. L. Flanagan, R. G. Lyons, and M. R. Schroeder, and “Theory and Application of Digital Signal Processing ” (Prentice-Hall, 1996) with S. F. Boll. He is the past President of the Institute of Electrical and Electronics Engineers Speech Section, past President of the Speech and Hearing Society, past President of the Audio Engineering Society, past Vice-President of the Acoustical Society of America, and past President of the Speech Sciences Section of the Acoustical Society of America. He was honored with the IEEE Leon G. Asche Prize in 1998.
Dr. Rabiner is a member of the National Academy of Engineering and the National Academy of Sciences, a Fellow of the Institute of Electrical and Electronics Engineers, a Fellow of the Acoustical Society of America, a Fellow of the Speech and Hearing Society, and a Member of the National Academy of Sciences Teaching Academy.
Introduction
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A critical, unmet, and highly desired objective in speech recognition is the problem of noise robustness. The goal is to realize a strong signal in the presence of noise. A great deal of progress has been made in the past. However, the field remains very active. In this article, we provide a review of the recent progress and future challenges in speech recognition in noisy conditions. The review is divided into three major parts: (1) comparison of speech recognition systems on real noisy databases; (2) research on speech signal processing and analysis; and (3) methods for robust speech recognition.
Comparison of Speech Recognition Systems on Real Noisy Databases
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In evaluating speech recognition systems, there are two basic questions that arise: How does the system perform when there is noise and how is the recognition performance affected by the amount of noise? It is essential that the recognition performance be assessed in conditions that are as close as possible to the real noisy environment. The idea of establishing an acoustic noise database is discussed by A. Zisserman et al. [@Zisserman2003]. The experimental database was collected at two sites and consists of non-stationary Gaussian white noise, of different bandwidths, in the background of speech. The speech signal was modelled as a time-varying first-order multichannel AR model with adaptive smoothing and expansion.
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