LEARNING A BETTER REPRESENTATION OF SPEECH SOUND WAVES USING RESTRICTED BOLTZMANN MACHINES

Presented by: Navdeep Jaitly, Author(s): Navdeep Jaitly, Geoffrey Hinton, University of Toronto, Canada

State of the art speech recognition systems rely on pre-processed speech features such as Mel cepstrum or linear predictive coding coefficients that collapse high dimensional speech sound waves into low dimensional encodings. While these have been successfully applied in speech recognition systems, such low dimensional encodings may lose some relevant information and express other information in a way that makes it difficult to use for discrimination. Higher dimensional encodings could both improve performance in recognition tasks, and also be applied to speech synthesis by better modeling the statistical structure of the sound waves. In this paper we present a novel approach for modeling speech sound waves using a Restricted Boltzmann machine (RBM) with a novel type of hidden variable and we report initial results demonstrating phoneme recognition performance better than the current state-of-the-art for methods based on Mel cepstrum coefficients.

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Recorded:	2011-05-26 10:30 - 10:50, Club D
Added:	15. 6. 2011 08:59
Number of views:	63
Video resolution:	1024x576 px, 512x288 px
Video length:	0:24:35
Audio track:	MP3 [8.34 MB], 0:24:35