首页 用matlab实现的语音识别算法

用matlab实现的语音识别算法

上传者： aileenwei | 上传时间： 2019-12-21 19:23:33 | 文件大小： 745KB | 文件类型： rar
在本文中，我们将深入探讨如何使用MATLAB实现语音识别算法，这一技术在现代通信、智能家居、自动驾驶等领域有着广泛应用。MATLAB作为一个强大的数值计算和数据可视化平台，为语音处理提供了丰富的工具箱和函数，使得开发这样的系统变得相对容易。 我们要理解语音识别的基本流程。它通常包括以下几个步骤： 1. 预处理：这部分涉及到语音信号的采集，噪声去除，预加重（Preemphasis）等。预加重是一种提升高频成分的技术，有助于消除人声的频率响应不平坦现象。 2. 分帧与窗函数：将连续的语音信号切分成固定长度的帧，并应用窗函数（如汉明窗或海明窗）以减少帧间干扰。 3. 声谱分析：通过快速傅里叶变换（FFT）对每帧进行分析，得到频域表示，即梅尔频率倒谱系数（MFCC）。MFCC是一种模拟人类听觉特性的特征提取方法，能有效压缩频谱信息。 4. 特征提取：从声谱图中提取关键特征，如MFCC系数、delta和delta-delta系数，这些特征将作为识别模型的输入。 5. 训练模型：利用机器学习算法（如隐马尔可夫模型HMM，支持向量机SVM，深度神经网络DNN等）训练模型，建立语音到文本的映射关系。 6. 匹配与识别：将待识别语音的特征与训练好的模型进行比较，找出最匹配的标签，完成识别过程。 在MATLAB中，可以使用Signal Processing Toolbox和Audio Toolbox来完成上述任务。例如，`audioread`用于读取音频文件，`preemph`进行预加重，`hamming`生成窗函数，`melFilterBank`计算MFCC，`hmmtrain`训练HMM模型，`viterbi`进行HMM的维特比解码。 在提供的压缩包中，"speech recognition"可能包含了以下文件： - `training.m`: 这个文件可能包含了训练部分的代码，用于构建识别模型。可能涵盖了特征提取和模型训练的过程。 - `matching.m`: 匹配部分的代码，用于将新语音样本与训练好的模型进行匹配，输出识别结果。 - `demo.m`: 演示部分，通常会调用上述两个函数，用户可以通过运行此文件来体验整个语音识别系统。 通过理解并实践这些MATLAB代码，你可以深入掌握语音识别的核心算法和技术，为自己的项目或研究打下坚实的基础。不过，需要注意的是，实际应用中的语音识别可能还需要考虑到更多的因素，如发音多样性、背景噪声、说话速度等，这需要在模型训练和参数调整中不断优化。

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评论信息

  • YSU.ISE :
    都别下了，浪费。该 P 代码文件 C:\Users\jj\Desktop\speech recongition\blockFrames.p 是在 MATLAB 7.5 版(R2007b)之前生成的，且
    2019-06-05
  • wuwuwukkkkk :
    该代码是加密形式的，没办法看到具体代码，用于学习不太适合。
    2018-07-10
  • weixin_39380815 :
    还不错，应该有帮助
    2018-06-03
  • Deron_Kong :
    很不错的东西 值得学习
    2018-03-27
  • 霜冻召唤师 :
    觉得还不错，应该有帮助
    2018-01-06

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