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matlab环境下基于动态规划算法的语音识别系统!-Matlab environment based on dynamic programming algorithm Speech Recognition System!
Update : 2008-10-13 Size : 4.02kb Publisher : hhh

matlab环境下基于动态规划算法的语音识别系统!-Matlab environment based on dynamic programming algorithm Speech Recognition System!
Update : 2025-03-11 Size : 4kb Publisher : hhh

DL : 0
基于动态时间规划(DTW)的语音信号识别,MATLAB环境下的-Dynamic Programming (DTW) of the speech signal recognition, MATLAB environment
Update : 2025-03-11 Size : 1.88mb Publisher : 阿若

比较详尽的介绍了语音识别系统的实现过程,以及相关技术。 端点检测:基于短时能量和短时平均过零率的端点检测和基于倒谱特征的端点检测 特征参数提取:LPCC和MFCC 参数模板存储:HMM和N_Gram 识别阶段:DWT 各阶段的相关技术都给了详细的介绍,绝对是好东西!-More detailed introduction to the speech recognition system implementation process and related technologies. Endpoint Detection: Based on the average short-term energy and zero crossing rate short-term endpoint detection Cepstrum-based endpoint detection feature extraction: LPCC and MFCC parameter template is stored: HMM and N_Gram recognition phase: DWT-related technologies offer the various stages of described in detail, is absolutely a good thing!
Update : 2025-03-11 Size : 7.91mb Publisher : 断剑

小波分析诞生于20世纪80年代, 被认为是调和分析即现代Fourier分析发展的一个崭新阶段。众多高新技术以数学为基础,而小波分析被誉为“数学显微镜”,这就决定了它在高科技研究领域重要的地位。目前, 它在模式识别、图像处理、语音处理、故障诊断、地球物理勘探、分形理论、空气动力学与流体力学上的应用都得到了广泛深入的研究,甚至在金融、证券、股票等社会科学方面都有小波分析的应用研究-Wavelet analysis was born in the 1980s, is considered the modern harmonic analysis and Fourier analysis is the development of a new phase. Mathematical basis of many high-tech, and wavelet analysis as the " mathematical microscope" , which determines its high-tech research in an important position. Currently, it is pattern recognition, image processing, speech processing, fault diagnosis, geophysical exploration, fractal theory, aerodynamics and fluid mechanics have been applied on extensive research, even in the financial, securities, stocks and other social sciences aspects of the application of wavelet analysis
Update : 2025-03-11 Size : 49.12mb Publisher : zhouxiaolin

In this paper the analysis of the compression process was performed by comparing the compressed signal against the original signal. To do this the most powerful speech analysis and compression techniques such as Linear Predictive Coding (LPC) and Discrete Wavelet Transform (DWT) was implemented using MATLAB. Here nine samples of spoken words are collected from different speakers and are used for implementation. The results obtained from LPC were compared with other compression technique called Discrete Wavelet Transform. Finally the results were evaluated in terms of compressed ratio (CR), Peak signal-to-noise ratio (PSNR) and Normalized root-mean square error (NRMSE).The result shows that DWT performance was better for these samples than the LPC method.
Update : 2025-03-11 Size : 144kb Publisher : Ambika

this a matlab code for speech recognition using discrete wavelet transform. code is working very well and good for future research work.-this is a matlab code for speech recognition using discrete wavelet transform. code is working very well and good for future research work.
Update : 2025-03-11 Size : 783kb Publisher : vikky

DL : 0
Discrete Wavelet Transform based speech compression technique with zero percent overlap haar mother wavelet.
Update : 2025-03-11 Size : 101kb Publisher : jskumar

The Discrete Wavelet Transform (DWT) is a transformation that can be used to analyze the temporal and spectral properties of non-stationary signals like audio. In this paper we describe some applications of the DWT to the problem of extracting information non-speech audio. More specifically automatic classification of various types of audio using the DWT is described and compared with other traditional feature extractors proposed in the literature. In addition, a technique for detecting the beat attributes of music is presented. Both synthetic and real world stimuli were used to uate the performance of the beat detection algorithm.-The Discrete Wavelet Transform (DWT) is a transformation that can be used to analyze the temporal and spectral properties of non-stationary signals like audio. In this paper we describe some applications of the DWT to the problem of extracting information non-speech audio. More specifically automatic classification of various types of audio using the DWT is described and compared with other traditional feature extractors proposed in the literature. In addition, a technique for detecting the beat attributes of music is presented. Both synthetic and real world stimuli were used to uate the performance of the beat detection algorithm.
Update : 2025-03-11 Size : 73kb Publisher : ali khaleel

convert the given speech to a text by using DWT , MfCC and LPC method to extract , match the given speech and convert it to the written text
Update : 2025-03-11 Size : 794kb Publisher : aaaaahmeeed

DL : 0
speech recognition using DWT
Update : 2025-03-11 Size : 285kb Publisher : abo_osamah
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