论文标题
基于深度学习的口吃诊断和治疗系统
Stutter Diagnosis and Therapy System Based on Deep Learning
论文作者
论文摘要
口吃,也称为结结巴巴,是一种传达障碍,破坏了演讲的连续性。该工作计划是试图制定自动识别程序来评估口吃的失调,并使用这些评估来滤除个人的语音疗法。口吃可能是以声音和音节的重复,延长或异常停止的形式。我们的系统旨在通过诊断口吃的严重程度和类型来帮助口吃者,并通过学习切割描述符和语音疗法对它们的有效性之间的相关性来提出适当的实践疗法。本文着重于使用封闭的复发CNN在MFCC音频功能上使用口径诊断剂实施,并使用SVM实施了治疗剂和治疗建议剂。它还介绍了获得的结果以及制定的各种关键发现。
Stuttering, also called stammering, is a communication disorder that breaks the continuity of the speech. This program of work is an attempt to develop automatic recognition procedures to assess stuttered dysfluencies and use these assessments to filter out speech therapies for an individual. Stuttering may be in the form of repetitions, prolongations or abnormal stoppages of sounds and syllables. Our system aims to help stutterers by diagnosing the severity and type of stutter and also by suggesting appropriate therapies for practice by learning the correlation between stutter descriptors and the effectiveness of speech therapies on them. This paper focuses on the implementation of a stutter diagnosis agent using Gated Recurrent CNN on MFCC audio features and therapy recommendation agent using SVM. It also presents the results obtained and various key findings of the system developed.