Semantic recognition model, training method thereof and semantic recognition method

Abstract

The embodiment of the invention provides a semantic recognition model, a training method thereof and a semantic recognition method. The model comprises an acoustic module which comprises a feature filtering layer, a phoneme feature layer and a word feature layer which are connected in sequence; a semantic module which comprises a full connection layer, a Transform layer and a logistic regression layer which are connected in sequence; and an acoustic module which is configured to extract phoneme feature vectors and word feature vectors in the audio data; the semantic module is configured to output semantic tags corresponding to the audio data according to the phoneme feature vectors and the word feature vectors, and the semantic tags are used for indicating semantic categories corresponding to the audio data.

Description

technical field [0001] The present application relates to the technical field of data processing, in particular, to a semantic recognition model, a training method thereof, and a semantic recognition method. Background technique [0002] At present, in the field of outbound robots / coordinated robots, speech semantic recognition generally adopts converting speech signals into text through Automatic Speech Recognition (ASR) technology, and judging the semantics through the text. [0003] In the process of converting speech into text through ASR technology, on the one hand, there is a certain error rate; on the other hand, the same ASR model adapts differently to different speech types. Speech types such as expressions and dialects have different adaptation effects, or the same ASR model also has different adaptation effects for speech recognition in the financial field and speech recognition in the education field. Interpreting the user's semantics through the text identified...

AI Technical Summary

Problems solved by technology

[0005] The embodiment of the present application provides a semantic recognition model, its training method, and a semantic recognition method, so as to

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