With the advent of `multimedia', digital signal processing (DSP) of sound has emerged from the shadow of bandwidth limited speech processing to become a research field of its own. To date, most research in DSP applied to sound has been concentrated on speech, which is bandwidth limited to about 4 kilohertz. Speech processing is also limited by the low fidelity typically expected in the telephone network. Today, the main applications of audio DSP are high quality audio coding and the digital generation and manipulation of music signals. They share common research topics including perceptual measurement techniques and analysis/synthesis methods. Additional important topics are hearing aids using signal processing technology and hardware architectures for digital signal processing of audio. In all these areas the last decade has seen a significant amount of application-oriented research. The frequency range of wideband audio has an upper limit of 20 kilohertz and the resulting difference in frequency range and Signal to Noise Ratio (SNR) due to sample size must be taken into account when designing DSP algorithms. There are whole classes of algorithms that the speech community is not interested in pursuing or using. These algorithms and techniques are revealed in this book. This book is suitable for advanced level courses and serves as a valuable reference for researchers in the field. Interested and informed engineers will also find the book useful in their work.

Applications of digital signal processing to audio and acoustics

High quality speech at low bit rates (e.g., 2400 bits/s) is one of the important objectives of current speech research. As part of long range activity on this problem, we have developed an efficient computer program that will serve as a tool for investigating whether articulatory speech synthesis may achieve this low bit rate. At a sampling frequency of 8 kHz, the most comprehensive version of the program, including nasality and frication, runs at about twice real time on a Cray-1 computer.

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A hybrid time-frequency domain articulatory speech synthesizer

This paper reviews methods for mapping from the acoustical properties of a speech signal to the geometry of the vocal tract that generated the signal. Such mapping techniques are studied for their potential application in speech synthesis, coding, and recognition. Mathematically, the estimation of the vocal tract shape from its output speech is a so-called inverse problem, where the direct problem is the synthesis of speech from a given time-varying geometry of the vocal tract and glottis. Different mappings are discussed: mapping via articulatory codebooks, mapping by nonlinear regression, mapping by basis functions, and mapping by neural networks. Besides being nonlinear, the acoustic-to-geometry mapping is also nonunique, i.e., more than one tract geometry might produce the same speech spectrum. The authors show how this nonuniqueness can be alleviated by imposing continuity constraints. >

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Techniques for estimating vocal-tract shapes from the speech signal

A digital simulation method of the vocal-tract system

Originally presented as the author's thesis (Ph. D.--Massachusetts Institute of Technology), 1985.

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The acoustics of fricative consonants

A traditional model of the speech signal has provided the underpinning of vocoder technology since the inception of analysis/synthesis telephony. The model is a first‐order approximation to human speech generation in which the source of vocal sound and the resonant acoustic system are treated as linear, separable elements. This source‐system model cannot properly account for a number of acoustic factors now known to exist in speech generation. We propose and implement here a signal model based more directly upon the phsyics of of speech generation. We also implement parametric control of the synthesis model by an adaptive procedure that minimizes the spectral difference between a human speech input and the synthetic output of the model.The adapted parameters constitute a low bit‐rate representation of the input human speech. We test a preliminary form of the system by computer simulation and demonstrate that in simple inital trials the signal model is able to adapt in a realistic manner.

Signal models for low bit‐rate coding of speech

Abstruct-Zero-crossing analysis techniques have long been applied to speech analysis, to automatic speech recognition, and to many other signal-processing and pattern-recognition tasks. In this paper, a mathematical formulation for each of several zero-crossing feature extraction techniques is derived and related (where possible) to each of the other zero-crossing methods Based upon this mathematical formulation, a physical interpretation of each analysis technique is effected, as is a discussion of the properties of each method. It is shown that four of these methods are a description of a short-time waveform in which essentially the same information is preserved. Each turns out to be a particular normalization of a count of zero-crossing intervals method. The effects of the various forms of normalization are discussed. A fifth method is shown to be a different type of measure; one which preserves information concerning the duration of zero-crossing intervals rather than their absolute number. Although reference is made as to how each of the zero-crossing methods has been applied to automatic speech recognition, an attempt is made to enumerate general characteristics of each of the techniques so as to make the mathematical analysis generally applicable.

K. Ishizaka

Papers

Signal models for low bit‐rate coding of speech

A Mathematical Formulation and Comparison of Zero-Crossing Analysis Techniques which have been Applied to Automatic Speech Recognition