Abstract
A dynamic programming-based optimization strategy for a temporal decomposition (TD) model of speech and its application to low-rate speech coding in storage and broadcasting is presented. In previous work with the spectral stability-based event localizing (SBEL) TD algorithm, the event localization was performed based on a spectral stability criterion. Although this approach gave reasonably good results, there was no assurance on the optimality of the event locations. In the present work, we have optimized the event localizing task using a dynamic programming-based optimization strategy. Simulation results show that an improved TD model accuracy can be achieved. A methodology of incorporating the optimized TD algorithm within the standard MELP speech coder for the efficient compression of speech spectral information is also presented. The performance evaluation results revealed that the proposed speech coding scheme achieves 50%-60% compression of speech spectral information with negligible degradation in the decoded speech quality.
Highlights
While practical issues such as delay, complexity, and fixed rate of encoding are important for speech coding applications in telecommunications, they can be significantly relaxed for speech storage applications such as store-forward messaging and broadcasting systems
We have proposed a dynamic programming-based optimization strategy for a modified temporal decomposition (TD) model of speech
Model accuracy control through TD resolution, and overlapping speech parameter buffering technique for continuous speech analysis can be highlighted as the main features of the proposed method
Summary
While practical issues such as delay, complexity, and fixed rate of encoding are important for speech coding applications in telecommunications, they can be significantly relaxed for speech storage applications such as store-forward messaging and broadcasting systems. Where the kth column of matrix A contains the kth event target vector, ak, and the nth column of the matrix Y (approximation of Y) contains the nth speech parameter frame, y(n), produced by the TD model. The results of the spectral stability-based event localizing (SBEL) TD [9, 10] and Atal’s original algorithm [6] for TD analysis show that event function overlapping beyond two adjacent event functions occurs very rarely, in the generalized TD model overlapping is allowed to any extent.
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