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Proceedings ArticleDOI

Efficient coding of LPC parameters by temporal decomposition

Bishnu S. Atal
- Vol. 8, pp 81-84
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TLDR
The aim is to determine the extent to which the bit rate of LPC parameters can be reduced without sacrificing speech quality.
Abstract
This paper describes a method for efficient coding of LPC log area parameters. It is now well recognized that sample-by-sample quantization of LPC parameters is not very efficient in minimizing the bit rate needed to code these parameters. Recent methods for reducing the bit rate have used vector and segment quantization methods. Much of the past work in this area has focussed on efficient coding of LPC parameters in the context of vocoders which put a ceiling on achievable speech quality. The results from these studies cannot be directly applied to synthesis of high quality speech. This paper describes a different approach to efficient coding of log area parameters. Our aim is to determine the extent to which the bit rate of LPC parameters can be reduced without sacrificing speech quality. Speech events occur generally at non-uniformly spaced time intervals. Moreover, some speech events are slow while others are fast. Uniform sampling of speech parameters is thus not efficient. We describe a non-uniform sampling and interpolation procedure for efficient coding of log area parameters. A temporal decomposition technique is used to represent the continuous variation of these parameters as a linearly-weighted sum of a number of discrete elementary components. The location and length of each component is automatically adapted to speech events. We find that each elementary component can be coded as a very low information rate signal.

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Citations
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Proceedings ArticleDOI

Spectral stability based event localizing temporal decomposition

TL;DR: In S/sup 2/ BEL-TD, the event localization is performed based on a maximum spectral stability criterion, which overcomes the instability problem of events of the Atal's method and results in a computationally simpler algorithm of TD.
Journal ArticleDOI

Transform representation of the spectra of acoustic speech segments with applications. I. General approach and application to speech recognition

TL;DR: An approach to modeling and capturing the time-varying structure of the spectral envelope of speech is reported, and the performance of the recognition algorithm based on this approach compares favorably with that of other techniques.
Patent

Unit-selection text-to-speech synthesis using concatenation-sensitive neural networks

TL;DR: In this paper, a set of predicted acoustic model parameters of a second target unit can be determined based on a first candidate speech segment of a first target unit and a set linguistic features of the second target units.
Journal ArticleDOI

Spectral stability based event localizing temporal decomposition

TL;DR: In S2BEL-TD, the event localization is performed based on a maximum spectral stability criterion, which overcomes the high parameter sensitivity of events of Atal?s method, thus resulting in a computationally simpler algorithm for TD.
References
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Book

Digital Processing of Speech Signals

TL;DR: This paper presents a meta-modelling framework for digital Speech Processing for Man-Machine Communication by Voice that automates the very labor-intensive and therefore time-heavy and expensive process of encoding and decoding speech.
Book

Linear Prediction of Speech

John E. Markel, +1 more
TL;DR: Speech Analysis and Synthesis Models: Basic Physical Principles, Speech Synthesis Structures, and Considerations in Choice of Analysis.
Journal ArticleDOI

Speech analysis and synthesis by linear prediction of the speech wave.

TL;DR: Application of this method for efficient transmission and storage of speech signals as well as procedures for determining other speechcharacteristics, such as formant frequencies and bandwidths, the spectral envelope, and the autocorrelation function, are discussed.
Journal ArticleDOI

Predictive Coding of Speech at Low Bit Rates

TL;DR: A new class of speech coders are described which allow one to realize the precise optimum noise spectrum which is crucial to achieving very low bit rates, but also represent the important first step in bridging the gap between waveform coders and vocoders without suffering from their limitations.