Voice recognition method and system based on deep neural network acoustic model

Abstract

The invention discloses a voice recognition method and system based on a deep neural network acoustic model. The method comprises steps of carrying out the sliding windowing preprocessing operation of a to-be-recognized voice, and extracting acoustic features; constructing and training a deep neural network acoustic model; calculating a likelihood probability corresponding to the extracted acoustic features by using the deep neural network acoustic model; constructing a static decoding graph, a decoder constructing a directed acyclic graph containing all recognition results as a decoding network through the static decoding graph and the likelihood probability on the basis of a viterbi algorithm of dynamic programming, and a word graph of the state level being obtained from the decoding network and being determined to obtain the word graph of the word level; and obtaining an optimal cost path word graph of the word-level word graph, obtaining a word sequence corresponding to an optimal state sequence of the word graph, taking the word sequence as a final recognition result, and completing voice recognition. According to the method, gradient dispersion and gradient explosion caused by a complex structure network model can be solved, the word error rate is reduced while the decoding speed is ensured, and recognition accuracy is improved.

Description

technical field [0001] The invention belongs to the technical field of speech recognition, and in particular relates to a speech recognition method and system based on a deep neural network acoustic model. Background technique [0002] In recent years, with the rapid development of the artificial intelligence industry, speech recognition technology has received more and more attention from academia and industry. As a front-end technology in the field of speech interaction, speech recognition plays a vital role. It is widely used in many human-computer interaction systems, such as intelligent customer service systems, chat robots, personal intelligent assistants, and smart homes. [0003] In the classic speech recognition framework, an acoustic model is a set of HMM (Hidden Markov Model). Generally, the parameters of an HMM consist of three parts: initial probability, transition probability, and observation probability. According to the acoustic model, the logarithmic obser...

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Problems solved by technology

[0004] However, there are still some challenges in the current research. The acoustic model trained by the high-computational deep neural network will cause gradient dispersion and gradient explosion, and the recognition accuracy in low-task scenarios still needs to be improved.

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