feature of speech

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双语例句

• The feature distribution of speech and non-speech, and the form of change, are examined for speech detection.
  介绍和提出了一种基于x~2分布的突变检测和一种语音/非语音决策树；
• Difference technology can express the dynamic feature parameters of speech.
  差分技术可以体现语音特征参数的动态特征。
• According to the simulated results, the power spectrum of ARMA model is more accurate than that of AR model, which is more suitable to reflect the feature of speech signal. ( 4) ARMA model is used in CELP.
  由仿真可知，ARMA模型比AR模型的功率谱更加准确，更适合描述语音信号的特性。（4）将ARMA应用到CELP算法中。
• Speech recognition has wide use in the field of communication and so on. Speech feature parameter extraction is an important part of the speech recognition system.
  语音识别在通信等领域有着广泛的用途，其中语音特征参数提取是语音识别系统的一个重要组成部分。
• The problem of syntactic feature of speech is one of the important contents in syntactic study.
  词类的句法特征问题是句法研究中的重要内容之一。
• 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。
• 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。
• Finally, according to the feature of speech recognition system which is designed in the paper, the principle of DTW arithmetic is discussed to be the recognition method of this system and the simulation process of the recognition voice is done using this method.
  根据本文设计的语音识别系统的特点，确定了动态时间归正（DTW）语音识别方法作为本系统的识别方法，并采用该算法对要识别的语音进行了仿真，得到正确的识别结果。
• 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。
• 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。