feature of speech
英
美
[网络] 演讲特点
双语例句
- During simulation experiment, wavelet analysis technique is adopted to extract feature vectors of speech, the results show that SVM and FSVM have both higher correct recognition rate and shorter training time than RBF network.
在仿真实验中，采用小波分析方法提取语音特征向量，识别结果表明，SVM和FSVM比RBF网络具有较好的泛化性能，训练时间也大大缩减。 - MFCC feature extraction of speech based on pitch period
基于基音周期的语音MFCC参数提取 - In technology, speech features reflecting the physical and action feature of individuals arc distilled from speech and the identity of the speaker is automatically recognized according to these speech parameters.
从技术上主要是从说话人语音信息中提取反映说话人的生理和行为特征的语音参数，并根据这些语音参数自动识别说话人的身份。 - And then introduces the functions and key technologies of pre-processing 、 feature extraction pattern matching and post-processing of speech recognition. Improved methods have been proposed in view of problems existed in traditional methods.
然后分别介绍了语音识别的预处理、特征参数提取、模式匹配和后处理阶段的功能及其关键技术，并针对传统方法中存在的问题提出了改进方案。 - First of all, I analyse the prosodic feature of the news broadcasting speech with the knowledge of acoustic-phonetics. For example, the extending of the syllable duration is the acoustic characteristics of the pre-boundary syllables and the accented syllables.
首先，通过实验的手段分析了新闻播音语言韵律特征的语音声学表现：即音节时长的拉长是边界前音节和重音音节的声学征兆； - In the new model is established, the speech recognition system, the selection of the speech signal of speech signal feature extraction and recognition of speech signal analysis.
在新的模型下，建立了语音分析识别系统，对所选取的语音信号进行特征参数提取和语音信号分析识别。 - This paper uses wavelet theory in noise-robust feature extraction of speech recognition and introduces a feature extraction method based on Gauss wavelet filter. The Gauss wavelet filter with human critical frequency band is obtained by studying human auditory characteristics.
把小波理论应用于抗噪语音识别特征提取，提出了基于高斯小波滤波器的语音识别特征提取方法，通过对人耳听觉特性的研究，按照人耳临界带宽设计了一组高斯小波带通滤波器。 - Design and analysis of acoustic feature for corpus of speech synthesis
语音合成语料库的设计与声学特征分析 - In order to assess speech quality effectively, a new approach of feature extraction of speech signals, MFSC ( Mel-frequency spectral coefficient), was proposed on the basis of the speech perception model.
为了有效评价通信系统中的语音质量，基于语音感知分析，提出了Mel域上一种新的语音信号特征表示方法&MFSC（美尔谱系数）。 - Using the invariable characteristics of PCNN time series and entropy series of Spectrogram, people can extract the feature of speakers speech and recognize the speakers rapidly and effectively.
该方法将语谱图输入到PCNN后得到输出图像的时间序列及其熵序列作为说话人语音的特征，利用它的不变性实现说话人识别。