Speech recognition method and device, computer equipment and storage medium

Abstract

The invention relates to a speech recognition method and device, computer equipment and a storage medium, and relates to the technical field of speech recognition. The method comprises the following steps: processing a speech signal through an acoustic model to obtain a phoneme recognition result corresponding to each speech frame in the speech signal; suppressing and adjusting the probability of null output in the phoneme recognition result corresponding to each speech frame so as to reduce the ratio of the probability of null output in the phoneme recognition result to the probability of each phoneme; and inputting the adjusted phoneme recognition result corresponding to each speech frame into a decoded image to obtain a recognition text sequence corresponding to the speech signal. According to the scheme, the recognition accuracy of the model can be improved in a speech recognition scene in the field of artificial intelligence.

Description

technical field [0001] The present application relates to the technical field of speech recognition, in particular to a speech recognition method, device, computer equipment and storage medium. Background technique [0002] Speech recognition is a technology for recognizing speech as text, which has a wide range of applications in various artificial intelligence (AI) scenarios. [0003] The speech recognition framework usually includes an acoustic model part and a decoding part, wherein the acoustic model part is used to recognize the phonemes of each speech frame in the input speech signal, and the decoding part outputs the text of the speech signal through the recognized phonemes of each speech frame sequence. In related technologies, implementing an acoustic model through a Recurrent Neural Network Transducer (RNN-T) is one of the focuses of research in the industry. [0004] However, the RNN-T model introduces the concept of empty output in the phoneme recognition proc...

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Problems solved by technology

[0004] However, the RNN-T model introduces the concept of empty output in the phoneme recognition process, that is, it predicts that a speech frame does not contain a valid phoneme. The introduction of empty output will lead to an increase in the error rate of the subsequent decoding process in some application scenarios. , especially leading to an increase in deletion errors, affecting the accuracy of speech recognition

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