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运用matlab工具对语音信号处理的几个方面：短时能量、端点检测、和功率谱及短时傅立叶变换特性作了简单的分析。-use Matlab tool for voice signal processing areas : short-term energy and endpoint detection, and the power spectrum and the characteristics of short-time Fourier transform with a simple analysi
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关于希尔伯特黄频谱的计算程序，提取的频谱信息可用于语音识别、故障检测等。-on Hilbert Huang spectrum calculation procedures to extract information of the spectrum can be used for voice recognition, fault detection.
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频谱方差在语音信号端点检测中的应用，matlab程序，实用。-spectrum of the voice signal variance endpoint detection, the application procedures Matlab and practical.
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基于语音频谱分析的重要性以及众多端点检测的算法以示其重要性,Based on the importance of voice spectrum analysis and the various endpoint detection algorithm to show its importance
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端点检测（短时能量，短时平均幅度，短时过零率）,short-term spectrum，spectrogram，End point detection
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语言处理过程中的端点检测，频谱分析，加重及去噪增强处理的MATLAB源程序和结果，语音文件改成自己的即可（采样点匹配）。-Language processing, endpoint detection, spectrum analysis, emphasis and de-noising and enhancement results of the MATLAB source code, voice files can be changed to your own (matched sample
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基于子带频谱墒的语音端点检测程序,程序思路清晰易懂-Based on the sub-band spectrum entropy Speech Endpoint Detection procedures, procedures clear and understandable
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基于谱减法的语音端点检测，运用了语音增强，去噪，谱减，加窗，端点检测等方法-Spectral subtraction based speech endpoint detection, using a speech enhancement, denoising, minus spectrum, add windows, endpoint detection methods such as
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Harmonic Product Spectrum (HPS) algorithm for pitch detection.
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自己遍的一种用于单字音识别的端点检测代码，首先做mfcc,再利用功率谱的峰值作为语音的方法，结果给出的是有效的mf-For their kind words over the sound identification of endpoint detection code, using the energy spectrum of the peak short-sighted approach as a voice
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语音信号处理的一些函数，包括：短时谱、短时能量、短时过零率和端点检测。-Some of voice signal processing functions, including: short-term spectrum, short-term energy, short-time zero crossing rate and endpoint detection.
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语音增强是影响语音识别系统性能的重要成分。为了比较语音增强算法的性能，采用Matlab软件进行了数值仿真，对不同噪声环境下的语音用3种不同的方法进行降噪，采用信噪比、端点检测等方法来降噪效果，并对几种增强算法的性能进行了比较分析。结果表明，在变噪声环境下短时谱MMSE法最佳，谱减法和维纳滤波法各有优点。-Speech enhancement of voice recognition is an important component of system performance. In order
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为提高语音端点检测系统在低信噪(0 dB 以下) 下
检测的准确率, 提出了一种基于谱熵的端点检测算法。将每
帧信号分为16 个子带, 选取频谱分布在250～ 3. 5 kHz 并且
能量不超过该帧总能量90 的子带, 计算经过语音增强后的
子带能量以及各子带信噪比, 根据各子带信噪比的不同调整
其在整个谱熵计算过程中的权重, 然后平滑谱熵, 以最终的
谱熵作为端点检测的依据-To improve endpoint detection system in the low
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语音信号的频域分析，包括短时谱，倒谱，LPC谱的程序，以及端点检测的程序-Voice signals in the frequency domain analysis, including short-time spectrum, cepstrum, LPC spectrum procedures, as well as the endpoint detection procedures
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双门限和熵谱法结合的语音端点检测，效果明显更适合语音识别研究-Dual voice endpoint detection threshold and entropy spectrum combined, the effect was more suitable for speech recognition research
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