Deep belief network for large vocabulary continuous speech recognition

Abstract

A method is disclosed herein that includes an act of causing a processor to receive a sample, wherein the sample is one of spoken utterance, an online handwriting sample, or a moving image sample. The method also comprises the act of causing the processor to decode the sample based at least in part upon an output of a combination of a deep structure and a context-dependent Hidden Markov Model (HMM), wherein the deep structure is configured to output a posterior probability of a context-dependent unit. The deep structure is a Deep Belief Network consisting of many layers of nonlinear units with connecting weights between layers trained by a pretraining step followed by a fine-tuning step.

Description

technical field [0001] The present invention relates to deep trust networks for large vocabulary continuous speech recognition. Background technique [0002] Speech recognition has been the subject of extensive research and commercial development. For example, speech recognition systems have been incorporated into mobile phones, desktop computers, automobiles, etc. to provide specific responses to speech input provided by users. For example, in a mobile phone equipped with voice recognition technology, a user can speak the name of a contact listed on the mobile phone, and the mobile phone can initiate a call to that contact. In addition, many companies are currently using speech recognition technology to assist customers in identifying company employees, identifying problems with products or services, and the like. [0003] However, even after decades of research, the performance of automatic speech recognition (ASR) systems in real-world usage scenarios is still far from ...

AI Technical Summary

Problems solved by technology

For example, training a feed-forward ANN using only backpropagation does not make good use of more than two hidden layers

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