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Olifrine / 基于MATLAB的简单语音识别

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README
Apache-2.0

基于MATLAB的简单语音识别

介绍

​ 制作一款简易的MATLAB程序,可以对简单语音信号如“早上好”、“晚上好”等进行识别

项目成员分工介绍

  • Yao Yijie : HMM部分(修改现有代码)和APP部分
  • Ye Zihao : VAD算法,滤波器等的实现与参数调节
  • Zhang Ding : Compare函数的编写,VAD算法等参数调节,DTW识别语料库录制者

软件架构

  • 主界面:主要功能集合:录音,DTW识别,HMM识别,输入与识别音频的波形展示,录音可保存为文件
  • DTW演示:用于展示程序对输入信号的处理,包括DTW及VAD,最后展示频域波形
  • HMM演示:当source文件夹中存在对应的HMM识别出的音频文件时,展示输入音频与识别音频的时域与频域波形
  • HMM模型选择:选择models文件夹中的HMM模型用于语音识别,程序启动时默认为HMM.mat,若不存在且不选择会报错
  • 资源库:展示source文件夹内wav文件与mp3文件的波形、时长与采样频率

更新内容

2022-12-12

​ 软件部分:final_ver6.0

  1. 进行了最后的改动,bug修复以及适配展示用的电脑2020a版本
  2. 删除了分页按钮的回调函数(在2020a上不可用)并且改为底部刷新条
  3. 增加了HMM模型的选取界面,可以选择模型进行匹配

​ PPT完工

2022-12-11

​ 算法部分:

​ 训练了两个HMM新模型,但是效果并不是很好

​ 着手制作PPT

2022-12-10

​ 算法部分:

  1. 借助工具箱实现了HMM模型的训练并且可以识别输入的语音
  2. 与之前的VAD等算法相融合,体验式实现了HMM识别

​ 软件部分:ver5.0

  1. 随算法进行改动
  2. 添加了HMM识别的按钮
  3. 添加了HMM展示的分页
2022-12-09

​ 算法部分:

  1. 查阅HMM相关资料,借助GMM-HMM工具箱进行代码的修改与使用
  2. DTW部分添加了滤波器,滤除噪音

​ 软件部分:

  1. 利用AI合成获得了一些语样模板

2. 换成了橙白主题

2022-12-08

​ 算法部分:

  1. 调整了MFCC函数的参数,使数据库本人声音的识别率达到90%以上,实现了特定人的特征词识别
  2. 再度调整了VAD算法的部分参数

​ 软件部分:ver4.0

  1. 随算法进行了改动
  2. 添加了DTW响应波形的分页,用于课堂展示显示输入信号的处理过程
  3. 增加了主页面的播放按钮和停止按钮,可以对声音进行播放
2022-12-06

​ 算法部分:

  1. 调整了VAD参数,暂时不使用MFCC
  2. 使用DTW实现了初步的语音识别,但成功率不高
  3. 确定了使用 Zhang Ding 的语音作为匹配模板

​ 软件部分:ver3.0

  1. 随算法进行了改动

2. 换成了黑绿主题

2022-12-03

​ 软件部分:ver2.0

  1. 添加了识别按钮,可以在时域对输入语音和模板音频进行DTW比较

  2. 添加分页与文件导入,可以在软件中导入模板音频用于比较

  3. 绘制了整体的UI,可以进行波形的展示与相关信息的输出

4. 换成了红蓝主题

​ 算法部分:初步实现MFCC参数提取的算法,互喷

2022-12-02

​ 算法部分:初步实现VAD算法

2022-12-01

​ 软件部分:ver1.0 录音部分软件编写

  1. 可进行采样频率与时长的设定

  2. 录制过程中指示灯变化

  3. 录制完成后自动显示波形

  4. 可以对录制的声音进行保存,文件名可自定义

  5. 程序启动时自动检测有无Recordings文件夹存在,若无则进行创建

​ 算法部分:搜集资料,初步确定方案

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