Chinese spoken language semantic comprehension method and system

Abstract

The embodiment of the invention provides a Chinese spoken language semantic comprehension method. The method comprises the steps of obtaining a generalized label-free text sequence training set, and performing forward prediction and reverse prediction on the training set in sequence to train a word-level and a word-level bidirectional language model; receiving spoken language voice audios input bya user, and carrying out sequence word segmentation to obtain character sequences and word sequences; decoding the character sequence and the word sequence by using the character-level bidirectionallanguage model and the word-level bidirectional language model respectively to obtain character-level implicit strata vectors and word-level implicit strata vectors; performing vector alignment on theimplicit strata vectors of the character sequence and the word sequence to obtain an implicit strata vector of spoken language voice audio input by the semantic comprehension model; and inputting thehidden layer vector of the spoken language voice audio into a semantic comprehension model, and determining the semantics of the spoken language voice audio. The embodiment of the invention further provides a Chinese spoken language semantic comprehension system. The embodiment of the invention has good generalization ability, combines word and character sequences, and improves the performance ofChinese semantic comprehension.

Description

technical field [0001] The invention relates to the field of intelligent voice interaction, in particular to a method and system for understanding the semantics of spoken Chinese. Background technique [0002] Semantic understanding plays an important role in the interaction of intelligent voice, and the following methods are usually used for semantic understanding: [0003] 1. Spoken language semantic understanding based on deep learning and supervised learning: It is necessary to carry out artificial semantic annotation on natural text or speech recognition text, combined with a deep neural network model, to train the semantic understanding model in a data-driven manner. [0004] 2. Spoken semantic understanding based on deep learning, supervised learning, and pre-trained word vectors: artificial semantic annotation needs to be performed on natural text or speech recognition text, and at the same time, external pre-trained word vectors are used to initialize the input laye...

AI Technical Summary

Problems solved by technology

[0014] In order to solve at least a large amount of manually-labeled data in the prior art, the word vector intelligently expresses the characteristics of a single word, which plays a very limited role in the generalization of sentences, and relies on a large number of high-quality unlabeled texts, language model training The data is huge, the training time is very long, the language model cannot consider characters and words at the same time, and the effect of Chinese word segmentation is not good

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