基于matlab的孤立词识别程序代码

以下是一个基于 MATLAB 的孤立词识别程序代码的示例：

% 孤立词识别程序

% 读取音频文件
[y,fs] = audioread('sample.wav');

% 预处理：降噪、去除静音等
% ...

% 特征提取
mfccs = melcepst(y, fs);

% 训练模型
% ...

% 识别
result = predict(model, mfccs);
disp(['识别结果：' result]);

其中，`sample.wav` 是需要识别的音频文件，`melcepst` 函数用于计算 MFCC 特征，`model` 是训练好的模型，`predict` 函数用于预测结果。需要根据具体情况修改代码中的参数和函数调用。

相关问题

基于matlab gui界面孤立词识别程序代码

以下是一个基于MATLAB GUI界面的孤立词识别程序代码的示例：

function varargout = isolated_word_recognition(varargin)
% ISOLATED_WORD_RECOGNITION MATLAB code for isolated_word_recognition.fig
%      ISOLATED_WORD_RECOGNITION, by itself, creates a new ISOLATED_WORD_RECOGNITION or raises the existing
%      singleton*.
%
%      H = ISOLATED_WORD_RECOGNITION returns the handle to a new ISOLATED_WORD_RECOGNITION or the handle to
%      the existing singleton*.
%
%      ISOLATED_WORD_RECOGNITION('CALLBACK',hObject,eventData,handles,...) calls the local
%      function named CALLBACK in ISOLATED_WORD_RECOGNITION.M with the given input arguments.
%
%      ISOLATED_WORD_RECOGNITION('Property','Value',...) creates a new ISOLATED_WORD_RECOGNITION or raises the
%      existing singleton*.  Starting from the left, property value pairs are
%      applied to the GUI before isolated_word_recognition_OpeningFcn gets called.  An
%      unrecognized property name or invalid value makes property application
%      stop.  All inputs are passed to isolated_word_recognition_OpeningFcn via varargin.
%
%      *See GUI Options on GUIDE's Tools menu.  Choose "GUI allows only one
%      instance to run (singleton)".
%
% See also: GUIDE, GUIDATA, GUIHANDLES

% Edit the above text to modify the response to help isolated_word_recognition

% Last Modified by GUIDE v2.5 16-Aug-2021 20:40:25

% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name',       mfilename, ...
                   'gui_Singleton',  gui_Singleton, ...
                   'gui_OpeningFcn', @isolated_word_recognition_OpeningFcn, ...
                   'gui_OutputFcn',  @isolated_word_recognition_OutputFcn, ...
                   'gui_LayoutFcn',  [] , ...
                   'gui_Callback',   []);
if nargin && ischar(varargin{1})
    gui_State.gui_Callback = str2func(varargin{1});
end

if nargout
    [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
    gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT


% --- Executes just before isolated_word_recognition is made visible.
function isolated_word_recognition_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject    handle to figure
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
% varargin   command line arguments to isolated_word_recognition (see VARARGIN)

% Choose default command line output for isolated_word_recognition
handles.output = hObject;

% Update handles structure
guidata(hObject, handles);

% UIWAIT makes isolated_word_recognition wait for user response (see UIRESUME)
% uiwait(handles.figure1);

% Initialize the recorder object
handles.recorder = audiorecorder(8000, 16, 1);

% Set the recording time to 1 second
handles.recording_time = 1;

% Update handles structure
guidata(hObject, handles);


% --- Outputs from this function are returned to the command line.
function varargout = isolated_word_recognition_OutputFcn(hObject, eventdata, handles) 
% varargout  cell array for returning output args (see VARARGOUT);
% hObject    handle to figure
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Get default command line output from handles structure
varargout{1} = handles.output;


% --- Executes on button press in record_button.
function record_button_Callback(hObject, eventdata, handles)
% hObject    handle to record_button (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Start recording
record(handles.recorder, handles.recording_time);

% Change the button text
set(hObject, 'String', 'Recording...');

% Update handles structure
guidata(hObject, handles);


% --- Executes on button press in stop_button.
function stop_button_Callback(hObject, eventdata, handles)
% hObject    handle to stop_button (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Stop recording
stop(handles.recorder);

% Change the button text
set(handles.record_button, 'String', 'Record');

% Get the audio data
audio_data = getaudiodata(handles.recorder);

% Play back the recorded audio
soundsc(audio_data, handles.recorder.SampleRate);

% Extract MFCC features from the audio data
mfcc_features = mfcc(audio_data, handles.recorder.SampleRate);

% Classify the spoken word using a pre-trained model
load('isolated_word_recognition_model.mat'); % Load the model
predicted_label = predict(model, mfcc_features'); % Predict the label

% Display the predicted label
set(handles.predicted_label_text, 'String', predicted_label);

% Update handles structure
guidata(hObject, handles);

在此示例中，我们使用MATLAB的GUI工具箱创建了一个GUI界面。用户可以使用该界面录制一个单词的语音，并使用预先训练的模型对该单词进行分类。该界面包括以下组件：

- `record_button`：开始录制语音的按钮
- `stop_button`：停止录制语音的按钮
- `predicted_label_text`：显示预测单词标签的文本框

在此示例中，我们使用了一个名为`mfcc`的函数来提取MFCC特征。这是一个自定义函数，需要单独下载和添加到MATLAB路径中。此外，我们还使用了一个预先训练的分类模型，该模型将MFCC特征映射到单词标签。该模型存储在一个名为`isolated_word_recognition_model.mat`的MAT文件中，需要与此代码一起下载并保存在同一目录中。

基于dtw的孤立词语音识别matlab

孤立词语音识别是一种常见的语音识别任务，而动态时间规整（DTW）是一种有效的算法用于处理语音信号的时间维度。在MATLAB中，可以利用DTW算法来实现基于孤立词的语音识别系统。

首先，需要准备一组包含已知发音的孤立词语音样本作为训练集。这些样本需要经过特征提取和预处理，例如MFCC（Mel频率倒谱系数）等，以便将其转化为计算机能够处理的数字形式。

接下来，利用MATLAB中的DTW算法对训练集中的各个孤立词语音样本进行模式匹配。DTW算法可以计算两个语音信号之间的距离，从而找到最相似的识别结果。这一过程需要使用动态规划来寻找最佳匹配路径，并得到最小距离值。

最后，可以通过设定阈值来进行识别结果的判断。当输入的语音信号与训练集中的孤立词相似度高于设定的阈值时，即可判定为成功识别。同时，也可以通过多个样本的累积比对来提高识别的准确性。

综上所述，利用MATLAB中的DTW算法可以实现基于孤立词的语音识别系统，通过训练集的模式匹配和阈值判定来实现对输入语音的识别和判断。这种方法在实际应用中具有一定的准确性和可靠性，适用于日常生活中的语音识别任务。