Voice recognition self-adaption method and system based on cache language model

Abstract

The invention discloses a voice recognition self-adaption method and system based on a cache language model, and belongs to the field of voice recognition. The method comprises the following steps: receiving a continuous voice signal input by a user, segmenting the continuous voice signal into a plurality of short voices based on a voice activity detection technology VAD, sequentially identifyingthe short voices based on a universal language model, generating a corresponding identification result for each short voice, searching based on a keyword to obtain an associated word table, and processing the associated word table through a cache model to obtain a language model adapting to the local change of historical recognition text distribution, and continuing to recognize subsequent short voices based on the modified language model. After local modification, the language model and the historical recognition content have better similarity, and the accuracy of continuous long speech recognition is improved. In addition, the user can actively correct the low-frequency words which are wrongly recognized in the recognition process, and the subsequent recognition accuracy of the low-frequency words is improved.

Description

technical field [0001] The invention relates to the field of speech recognition, in particular to an adaptive method and system for speech recognition based on a cached language model. Background technique [0002] After decades of development, speech recognition has become a relatively mature technology. In practical applications, Siri, Cortana, etc. have high recognition accuracy under ideal conditions. [0003] The performance of a speech recognition system largely depends on the similarity between the language model (LM) used and the task to be processed. This similarity is especially important in cases where the statistical properties of language change over time, such as in application scenarios involving spontaneous and multi-domain speech. Topic Identification (TI) based on information retrieval is a key technology. The topic under discussion can be obtained through semantic analysis of historical recognition results, so as to adjust the language model and realize d...

AI Technical Summary

Problems solved by technology

[0006] In order to solve the problem that the existing speech recognition system based on single-sentence tasks cannot adapt to domain information to recognize low-frequency words, resulting in low recognition accuracy of low-frequency words or too complicated recognition system, the present invention proposes a speech recognition system based on a cached language model. Identify adaptive methods and systems

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