Publication

Phase AutoCorrelation (PAC) features in Entropy based Multi-Stream for Robust Speech Recognition

Hervé Bourlard
2003
Reports

Abstract

Methods to improve noise robustness of speech recognition systems often result in degradation of recognition performance for clean speech. Recently proposed Phase AutoCorrelation (PAC) \cite{ikbal03,ikbal03a} based features, showing noticeable improvement in noise robustness, also suffer from this draw back. In this paper, we try to alleviate this problem by using the PAC based features along with regular speech features in a multi-stream framework. The multi-stream system uses entropy of the posterior probability distribution, computed during recognition, as a confidence measure to adaptively combine evidences from different feature streams \cite{misra03}. Experimental results obtained on OGI Numbers95 database and Noisex92 noise database show that such a system yields best possible recognition performance in all conditions. Actually, the combination always performs better than the best performing stream for all the conditions.

Official source

https://infoscience.epfl.ch/entities/publication/4026de22-e940-4839-b98c-3681d6eff4db

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Hervé Bourlard
2003
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/4026de22-e940-4839-b98c-3681d6eff4db

About this result

Related concepts (32)

Speech recognition

Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition (ASR), computer speech recognition or speech to text (STT). It incorporates knowledge and research in the computer science, linguistics and computer engineering fields. The reverse process is speech synthesis.

Prior probability

A prior probability distribution of an uncertain quantity, often simply called the prior, is its assumed probability distribution before some evidence is taken into account. For example, the prior could be the probability distribution representing the relative proportions of voters who will vote for a particular politician in a future election. The unknown quantity may be a parameter of the model or a latent variable rather than an observable variable.

Posterior probability

The posterior probability is a type of conditional probability that results from updating the prior probability with information summarized by the likelihood via an application of Bayes' rule. From an epistemological perspective, the posterior probability contains everything there is to know about an uncertain proposition (such as a scientific hypothesis, or parameter values), given prior knowledge and a mathematical model describing the observations available at a particular time.

Related publications (2)

Phase AutoCorrelation (PAC) features in Entropy based Multi-Stream for Robust Speech Recognition

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