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activlev.rar
- calculates the active level of a speech segment according to ITU-T recommendation P.56.,calculates the active level of a speech segment according to ITU-T recommendation P.56.
Peter_Howell_Stevie_Sackin_and_Kazan_Glenn
- Peter Howell, Stevie Sackin, and Kazan Glenn Development of a Two-Stage Procedure for the Automatic Recognition of Dysfluencies in the Speech of Children Who Stutter: II. ANN Recognition of Repetitions and Prolongations With Supplied Word Segment M
vad
- 在语音识别系统中，端点检测的目的是要区分语音段和非语音段 ,它在自动语音识别中起着关键作用-In speech recognition systems, the purpose of endpoint detection is to distinguish between voice and non-voice segment, which in automatic speech recognition plays a key role
gazetracking
- gaze tracking system in matlab-used to move a wheelchair for physically challenged people
silenceRemoval
- his a simple method for silence removal and segmentation of audio streams that contain speech. The method is based in two simple audio features (signal energy and spectral centroid). As long as the feature sequences are extracted, as thresholding app
imm3851
- This project describes the work done on the development of an audio segmentation and classification system. Many existing works on audio classification deal with the problem of classifying known homogeneous audio segments. In this work, audio recordi
fenge
- 用于提取一段语音中单独的每个字词，matlab下编写，经检测很好用。注意不是检测一段语音中的一个字词，而是所有的。-to detect every word in a speech segment. made in matlab. mind you , it s meant to detect every word, not only the first word
activlev
- 根据国际电信联盟标准P.56对语音信号的作用电平进行计算。-calculates the active level of a speech segment according to ITU-T recommendation P.56
Kalmanflt
- filter for denoising noisy speech (corrupted by white noise). Kalman filtering of noisy speech usually have two steps: Estimating the AR parameters of speech segment
segment
- 语音分段检测算法，用于语音增强的分段算法。-Speech segment detection algorithm, the segmentation algorithm for speech enhancement.
Speech-signal-classification-
- 语音特征信号识别是语音识别研究领域中的一个重要方面,一般采用模式匹配的原理解 决。语音识别的运算过程为:首先,待识别语音转化为电信号后输入识别系统,经过预处理后用数学方法提取语音特征信号,提取出的语音特征信号可以看成该段语音的模式。然后将该段语音模型同已知参考模式相比较,获得最佳匹配的参考模式为该段语音的识别结果.-Speech characteristic signal recognition is an important aspect in the field of speech re
classification-of-Speech-signal-
- 语音特征信号识别是语音识别研究领域中的一个重要方面,一般采用模式匹配的原理解 决。语音识别的运算过程为:首先,待识别语音转化为电信号后输入识别系统,经过预处理后用数学方法提取语音特征信号,提取出的语音特征信号可以看成该段语音的模式。然后将该段语音模型同已知参考模式相比较,获得最佳匹配的参考模式为该段语音的识别结果-Recognition is the speech characteristic signal in the field of speech recognition is an i
POSTaggingObservations
- top the acquisition and displays the recognized digit. Beside the length of speech segment it is possible to change also the sampling frequency.
exercise317
- On the companion website, you will find in the directory Chap exercises/chapter3 a workspace ex3M1.mat. Load this workspace and plot the speech waveform labeled speech1 10k. This speech segment was taken a vowel sound that is approximately period
speech
- 可以直接用于MATLAB中的.txt语音片段，浊音片段-Can be directly used for.Txt speech segments MATLAB, voiced segment
speech reconstruction+SLP
- This paper proposes a new variant of the least square autoregressive (LSAR) method for speech reconstruction, which can estimate via least squares a segment of missing samples by applying the linear prediction (LP) model of speech. First, we show t
Speech Encoding - Frequency Analysis MATLAB
- The speech signal for the particular isolated word can be viewed as the one generated using the sequential generating probabilistic model known as hidden Markov model (HMM). Consider there are n states in the HMM. The particular isolated speech sig
107551700第三次作业
- 根据短时傅里叶变换原理，计算并显示该语音段的短时幅度谱和功率谱；根据语谱图显示原理，编程实现该语音段语谱图的计算和显示，并尽量多地分析出语谱图包含的语音特征信息,用MATLAB提供的倒谱计算函数，显示该语音段的复倒谱和倒谱(According to the principle of short-term Fourier transform, the short range spectrum and power spectrum of the speech segment are calculat
ABSE
- 熵值越大则每个符号包含的平均信息量越大。有研究发现，在有噪声的语音信号中，语音信号的熵和噪声信号的熵存在着较大的差异，对噪声信号来说在整个频带内分布相对平坦，熵值小，语音信号集中在某些特定频段内，熵值大。因此利用这个差异可以区分噪音段和语音段。(The greater the entropy is, the greater the average information of each symbol is. It is found that, in noisy speech signals, t
audio_tezheng
- 语音信号的时域、频域与倒谱域分析。 1.分析一帧清音和浊音的自相关函数和倒谱系数 2.用Matlab画出该段语音的时域波形、短时能量、短时平均幅度、短时过零率、短时过电平率 3.选择一帧无声、清音和浊音的语音，用Matlab画出它们的对数幅度谱(Time domain, frequency domain and cepstrum domain analysis of speech signals. 1. Analyze the autocorrelation function and c