The management of mental health in a smart medical dialogue system based on a two-stage attention speech enhancement module

IF 3.1 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Computer Speech and Language Pub Date : 2025-02-10 DOI:10.1016/j.csl.2025.101778

Yongtai Quan

{"title":"The management of mental health in a smart medical dialogue system based on a two-stage attention speech enhancement module","authors":"Yongtai Quan","doi":"10.1016/j.csl.2025.101778","DOIUrl":null,"url":null,"abstract":"<div><div>In response to the current problems in artificial intelligence medical conversations, this project intends to study a new speech reinforcement algorithm based on two-level attention mechanism to improve its correct understanding and execution ability in verbal communication. Firstly, the paper conducted theoretical and research on commonly used speech enhancement algorithms, and studied them; On this basis, a speech enhancement algorithm based on a two-level attention reinforcement network is studied, and the algorithm is applied to intelligent medical conversations. The experiment shows that the signal-to-noise ratio obtained by using a two-level attention enhancement algorithm is higher than that of the attention enhancement algorithm for a 3 dB channel, with a 3 % improvement in signal-to-noise ratio, which is the smallest among the four algorithms. In addition, 98.95 % of students reported participating in 13 or more tutoring classes, accounting for 54 %. Based on the above research, the proposed speech enhancement method in this article is feasible and effective.</div></div>","PeriodicalId":50638,"journal":{"name":"Computer Speech and Language","volume":"92 ","pages":"Article 101778"},"PeriodicalIF":3.1000,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Speech and Language","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0885230825000038","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}

引用次数: 0

Abstract

In response to the current problems in artificial intelligence medical conversations, this project intends to study a new speech reinforcement algorithm based on two-level attention mechanism to improve its correct understanding and execution ability in verbal communication. Firstly, the paper conducted theoretical and research on commonly used speech enhancement algorithms, and studied them; On this basis, a speech enhancement algorithm based on a two-level attention reinforcement network is studied, and the algorithm is applied to intelligent medical conversations. The experiment shows that the signal-to-noise ratio obtained by using a two-level attention enhancement algorithm is higher than that of the attention enhancement algorithm for a 3 dB channel, with a 3 % improvement in signal-to-noise ratio, which is the smallest among the four algorithms. In addition, 98.95 % of students reported participating in 13 or more tutoring classes, accounting for 54 %. Based on the above research, the proposed speech enhancement method in this article is feasible and effective.

查看原文本刊更多论文

求助全文

约1分钟内获得全文求助全文

来源期刊

Computer Speech and Language 工程技术-计算机：人工智能

CiteScore

11.30

自引率

4.70%

发文量

审稿时长

22.9 weeks

期刊介绍： Computer Speech & Language publishes reports of original research related to the recognition, understanding, production, coding and mining of speech and language. The speech and language sciences have a long history, but it is only relatively recently that large-scale implementation of and experimentation with complex models of speech and language processing has become feasible. Such research is often carried out somewhat separately by practitioners of artificial intelligence, computer science, electronic engineering, information retrieval, linguistics, phonetics, or psychology.