耳语音

基于RBF神经网络的汉语耳语音转换为正常语音的研究

Research on Reconstruction of Chinese Normal Speech from Whispered Speech Based on RBF Neural Network

利用分数阶傅里叶变换提取耳语音特征参数的关键问题是如何确定最优阶数p来达到处理的最佳效果。

How to determine the optimal order of fractional Fourier transform to extract features of whispered speech in order to achieve the best result is a key issue .

文中针对带噪的耳语音提出了一种LMS自适应滤波的耳语音增强算法。

This paper introduces a method based on the combination of spectral subtraction and LMS adaptive filtering in the enhancement of Chinese whispered speech .

实验表明，修正MFCC参数可以作为表征耳语音特点的参数，它提高了耳语音话者识别系统的识别率。

As shown in the experiments , these modified MFCC can be used as the character parameter in the whispered speaker recognition . They improve the performance of the whispered speaker recognition systerm .

实验结果说明了MFCC结合幅值包络可作为汉语耳语音自动识别的特征参数，在小字库内用HMM模型识别得出的识别率为90.4%。

The experimental results demonstrate that the MFCC combined with the amplitude contour features can be used as efficient parameters for the Chinese whispered speech recognition . A recognition rate of 90.4 % is obtained when a small Chinese isolated word database is tested using HMM approach .

耳语音是人与人之间一种特殊的语音交流方式。

Whispered speech is a special communication style between Humans .

耳语音声调特征的研究

Study on the characteristics of the tones in whispered Chinese

最后利用采集到的语音库分析验证了耳语音的声学特性。

Using transfer matrix method to analyze microperforated panel absorbers acoustic characteristic ;

基于听觉模型的耳语音的声韵切分

Initial / final segmentation of Chinese whispered speech based on the auditory model

耳语音是一种有别于正常语音的发音模式。

Whispered speech is different from the normal speech in the pronunciation pattern .

耳语音转换为正常语音可以解决这个问题。

The problem can be resolved if the mobile communication products have the ability of converting the whispered speech into the normal speech .

耳语音作为正常音的补充和替代，是人们日常生活中广泛使用的语言交流方式之一。

Whispered speech , as a complement and substitute to the normal speech , is one of the widely used communication ways in daily life .

耳语音作为人类的一种特殊发音方式，在语音学和生理学上都有别于正常音。

Whispered speech , a special phonation mode different from normal speech in phonetics and physiology , has existed in human daily life for long time .

本文根据耳语音信号发音模型，结合耳语音的声学特性，建立了一个汉语耳语音孤立字识别系统。

In this paper , a Chinese isolated word recognition system is established based on the source-filter generation model combined with the acoustic characteristics of whispered speech .

但是由于耳语音本身的特点，如声级低、没有基频等，给耳语音识别研究带来了困难。

However , the characteristics of whispered speech such as its low sound pressure level and the lack of fundamental frequency bring difficulty to the whispered speech recognition .

另外，考虑到当一个正常音训练的说话人系统用耳语音识别时，系统的性能表现会急速下降。

In an addition to this problem , as a speaker system trained mainly by normal speech , the performance of system declines sharply as tested with whispered speech .

耳语音是人们在公众场合常使用的一种语言交流方式，初期对耳语音的研究主要为了语音基础研究和医学工作的需要。

The earlier study about whispers is focused on the phonetics and medicine , and now , more attention has been paid to applied research with the development of technology .

本文分析了耳语音的特点，并根据生理声学及心理声学的基本理论与实验资料，提出了一种利用听觉模型来进行耳语音声韵切分的方法。

In this paper , the characteristics of whispered speech are discussed , and a new approach for initial / final segmentation of Chinese whispered speech is proposed on the basis of psychological acoustic theories and experiments .

其具有声带不振动、基频缺失和声音能量低的特性，这些特性降低了耳语音的可懂度和清晰度。

The whispered speech has some characteristics different from normal speech , such as no vocal cords vibration , pitch disappearing and lower speech energy than that of the phonated speech . The special voicing style of whispery decreases the speech intelligibility and the speech quality .

本文将耳语音和正常音假设成两种不同的信道，在通用背景模型的基础上，对语音参数做特征映射后再进行训练和识别，以减少信道的影响。

In order to change this phenomenon , on the condition that whispered speech and normal speech come from different channels , feature mapping is used to reduce the effects of channels before training and testing speaker system based on the universal background model ( UBM ) .

利用人耳的语音感知特性中的掩蔽效应，提出一种基于听觉掩蔽效应的语音增强方法。

A new method based on masking properties of human ear for speech enhancement is proposed .

在增强算法中文中重点介绍了基于频谱减法的语音增强算法、基于最小均方误差（MMSE）的语音增强算法以及结合了人耳掩蔽效应的语音增强算法。

And then the thesis introduced traditional spectral subtraction algorithm and other two speech enhancement algorithms which based on Minimum Mean Square Error ( MMSE ) and associated with auditory masking properties .

一种基于短时谱估计和人耳掩蔽效应的语音增强算法

Speech Enhancement Based on Masking Properties and Short-Time Spectral Amplitude Estimation

一种基于人耳听觉特性的语音客观测度研究

Study of an Objective Quality Evaluation Measure Based on Acoustic Perception

基于人耳听觉模型的语音质量客观评价方法

Objective Evaluation Method of Speech Quality Based on Human Auditory Model

符合人耳听觉特性的语音音质的客观评价方法

Objective Assessment of Acoustic Fidelity of Speech Based on Characteristics of Human Hearing

然后对基于人耳掩蔽阈值的语音增强方法作了讨论。

Third we make detailed discussion on the method based on human auditory masking properties .

作为应用利用该系统实现了基于人耳听觉特性的语音信号预处理系统。

The speech signal preprocessing system based on the property of human 's sense of hearing was realized by using this system .

本文以语音增强为研究课题，提出基于人耳听觉特性和语音语谱特性的语音增强方法。

This paper studies the speech enhancement technology , and proposes an improved spectral subtraction method based on the auditory characteristics and the speech spectrum characteristics .

考虑到面向自主车应用的实际应用背景，本文研究了如何在含噪声环境下提高语音识别系统的性能，实现了一种结合人耳听觉特性和语音增强技术的鲁棒语音特征提取方法。

Considering the practical recognition system application , how to acquire satisfactory performance under noisy environment is an important part of this article . We present a robust speech feature extraction method based on properties of the human auditory system and speech enhancement algorithm .