基于HMM的可训练情感语音合成研究

研究了基于HMM的英语语音合成系统,并通过分析情感语句的语调,得到高兴、生气、悲伤、平静四种不同情感类型的基音频率、能量和时长的变化规律。将基于HMM的可训练语音合成方法应用到英语情感语音合成,对基于HMM所合成的平静语音,叠加相应情感的语调变化规则,通过修改韵律特征参数合成出带有情感的语音。从所合成情感语句的效果来看,合成语音整体稳定流畅、情感特征明显,且合成系统可以快速构成。     

情感语音合成的研究

介绍了语音信号中的情感语音合成的方法，通过分析情感语句的语调，得到了喜、怒、惊、悲4种情感不同的变调规律，对不同的情感类型确定相应的基音频率变化规律、能量变化规律、元音的变异规律和无声时延比例变化规则。对于待合成的语音，首先进行文本扫描，再叠加相应情感的语调变化规则，利用PSOLA算法进行情感语音合成，获得了较好效果。     

数据挖掘技术在情感语音合成中的应用

目前,情感语音合成自然度在情感语音合成中成为难点,本论文将数据挖掘技术应用于其中.通过对传统的Aprior算法的改进,探讨了如何提取情感语音韵律参数之间的关系,并且在理论上表明比传统Aprior算法在挖掘情感语音频繁项目集的效率高.应用这些规则,可以很方便的为以后情感语音合成系统的选音提供帮助和参考.     

语音合成系统中语音库样本能量均衡方法研究

语音库的质量是决定语音合成(Text to Speech, TTS)效果的重要因素。TTS语音库的制作周期需要六个月左右,期间,发音人的录音状态需要保持一致,即音色、能量皆不能有大的差异,这对于发音人来说是较为困难的。为此,本文给出语音能量均衡方法,其中包括时域包络波动检测算法和帧能量平均算法,旨在解决TTS语音数据库录制后能量不一致现象。首先分析获得标准语音的相关能量参数和波动参数作为模板;其次,利用时域包络波动检测算法对预调节语音样本的合格性进行检验;最后根据帧能量平均准则,对所有合格语音样本进行时域幅值调整,以最大限度地保证语音库整体能量的一致性。实验结果表明,本文提出的语音能量均衡方法可以有效提升TTS语音库质量,具有实际工程意义。      

结合音质特征和韵律特征的语音情感识别

为了提高语音情感的正确识别率,在情感语音韵律特征的基础上,提出情感语音音质特征的提取.结合音质特征参数和韵律特征参数,采用支持向量机分类器实现汉语普通话生气、高兴、悲伤和惊奇四种主要情感类型语音的情感识别.实验结果表明,语音音质特征参数和韵律特征参数相结合取得的情感平均正确识别率为88.1%,比单独使用韵律特征参数高出6%.可见,语音音质特征是一种较有效的情感特征参数.     

引用LSP分析参数的频谱感度客观评价语音音质

本文通过汉语语音ＬＳＰ分析参数的普频敏感度的研究，将敏感度与听觉特性相联系，用频谱敏感表表征语音中对听觉敏感的分析，从而给出了音质的定是描述，提供了对语音音质的客观评价方法。     

基于瑞利多径衰落信道的信号包络频谱感知

为提高信号采样值之间的相关性和降低噪声对感知性能的影响,该文提出基于信号包络自相关矩阵的频谱感知算法。首先对采样信号等间隔时长截取,以相邻间隔的采样值计算信号自相关性,并构造出近似自相关矩阵。其次依据矩阵次对角线元素性质构造了统计量。分别计算了该统计量的检测概率分布函数与虚警概率分布函数,分析了频谱感知算法的检测性能,算法优化了信号相关性的计算,降低了噪声对感知性能的影响。最后通过仿真验证了不同参数对检测概率和虚警概率的影响,并提出了进一步提高检测性能的措施。

     

层次韵律特征对语音情感转换的影响分析

针对传统以音节为单位的情感语音转换方法不能有效反映韵律的动态变化特征问题,分析音节、韵律词、语句三个层次的韵律特征在不同情感下的变换规律。根据情感语音的层次韵律变换特点,在音节层韵律转换的前提下,结合韵律词和语句级别的韵律变化特征对音节的韵律特征进行修正,实现语音的情感转换。实验结果表明,对于开心、生气和悲伤三种情感语音的转换,采用层次韵律转换的方法可以有效提高情感语音合成的质量。     

用于GMDSS中语音合成技术的研究

本文提出语言合成将作为面向二十一世纪发展的安全航行技术。介绍语音合成的三种方法。着重分析ＪＵＥ－４５Ａ船站中语音合成电路的工作原理及其对掌握该船站操作的指导作用。最后揭示了在ＧＭＤＳＳ中应用语音合成技术已引起船东的高度重视。     

语音情感转换技术综述

随着情感信息处理的研究不断深入,语音信号中的情感转换越来越受到人们的重视。与传统的信息处理技术不同,语音的情感转换是用机器来实现理解和认识。本文首先探讨了情感的分类;接着,将语音情感转换系统分为：特征提取、参数转换和语音合成,并从特征提取和参数转换两方面进行了阐述,分析了相关的理论及算法,对各方法的优缺点进行了比较。最后,对语音情感转换研究方向进行了讨论。     

基于多频带谱减法的抗噪声语音识别研究

为了减少在噪声环境下测试条件与训练条件不匹配导致的语音识别性能下降,提出了一种结合多频带谱减法的抗噪声语音识别系统。首先提取带噪语音的前几帧作为估计的噪声信号,将带噪语音、估计的噪声信号按频率划分M个互不相交的频带,然后根据每个频带内带噪语音与估计的噪声信号的性噪比,来确定该频带噪声的谱减参数。语音增强作为前端处理,与语音识别器级连构成抗噪声语音识别系统。通过实验仿真表明,基于多频带谱减法的抗噪声语音识别系统在不同信噪比不同类型的噪声下,识别性能明显优于基本谱减法。     

Emotional speech recognition based on SVM with GMM supervector

Emotion recognition from speech is an important field of research in human computer interaction. In this letter the framework of Support Vector Machines (SVM) with Gaussian Mixture Model (GMM) supervector is introduced for emotional speech recognition. Because of the importance of variance in reflecting the distribution of speech, the normalized mean vectors potential to exploit the information from the variance are adopted to form the GMM supervector. Comparative experiments from five aspects are conducted to study their corresponding effect to system performance. The experiment results, which indicate that the influence of number of mixtures is strong as well as influence of duration is weak, provide basis for the train set selection of Universal Background Model (UBM).     

Sequential speech segmentation based on the spectral ARMA transition measure

Sequential segmentation algorithms based on the AR model tend to produce false alarms or to omit the change for sequences that corresponds to the ARMA model. In this paper a new sequential segmentation algorithm based on the ARMA model is presented. The ARMA model is estimated over the relatively short sequence, which has called for the implementation of the estimation algorithm with appropriately initialized starting values. The proposed algorithm adopts the MGLR concept of the sliding reference and test windows, which allow the process of decision making to be separated from the evaluation of the discrimination function. This has enabled the new triangular decision rule to be proposed; this is based on the expected shape of the discrimination function at the time of the model change. Two possible discrimination functions have been suggested. One of them is optimal in the statistical sense; the other has the better asymptotic behavior. Natural speech signal segmentation is also discussed, and an appropriate pitch-synchronous signal prearrangement has been suggested. This not only enhances the segmentation algorithm but also increases its speed, as the time can be increased by a step equal to the pitch period. The segmentation algorithm is verified on test signals as well as on the natural speech signal. The experimental results also include a comparison of the sequential AR and ARMA model-based segmentation.Research supported in part by the Serbian Science Foundation, grant nos. 0403 and 1007.     

基于谱减算法语音增强的研究

谱减法是处理宽带噪声较为传统和有效的方法,它的运算量较小,容易实时实现,增强效果也较好.比较详细地叙述了谱减法的基本原理和降低音乐噪声的方法.     

Inter-frame interpolation of the spectral envelope of the speech signal in the space of linear spectral frequencies of the highest regression

Peculiarities of interpolating the parameters which encode the shape of the speech signal’s envelope in the space of linear spectral pairs on the example of the G.729 recommendation are considered. Results of experimental research of the interpolation processes of the similar parameters in the equivalent classical spaces are discussed. It is shown that carrying out the interpolation in the space of linear spectral parameters of the highest regression allows significantly decreasing the relative interpolation error if compared with interpolation in the classical spaces.     

改进的基于人耳掩蔽效应谱减语音增强算法

提出一种谱估计中的平滑系数自适应变化的新算法,该算法利用人耳掩蔽特性改进语音最小均方误差的对数谱估计增益和无语音概率(SAP)参数,并且利用改进后的SAP参数自适应地调节平滑系数,以求随着不同噪声环境的变化在去噪度、残留音乐噪声和语音畸变度之间自适应地折中.实验表明新算法相对于其他谱减法在相同的去噪度下,语音畸变度最小且几乎察觉不到音乐噪声.特别是在低信噪比的环境下,相对其他谱减法的优势更显著.     

Pushing the envelope - aside [speech recognition]

Despite successes, there are still significant limitations to speech recognition performance, particularly for conversational speech and/or for speech with significant acoustic degradations from noise or reverberation. For this reason, authors have proposed methods that incorporate different (and larger) analysis windows, which are described in this article. Note in passing that we and many others have already taken advantage of processing techniques that incorporate information over long time ranges, for instance for normalization (by cepstral mean subtraction as stated in B. Atal (1974) or relative spectral analysis (RASTA) based in H. Hermansky and N. Morgan (1994)). They also have proposed features that are based on speech sound class posterior probabilities, which have good properties for both classification and stream combination.     

基于证据源和组合规则双修正的证据合成

针对冲突证据合成时传统证据合成和单一修正改进方法存在的问题,提出了新的证据源预处理和组合规则双修正的证据合成方法.利用夹角余弦理论,计算证据向量距离后转化得到权重系数,依据权重系数对证据进行重新概率分配,完成证据源修正;之后用修正的动态合成规则,进行修正证据的合成;同时提出了算法分析模型.实例结果验证了算法在证据合成时具有最优结果、较强通用性和稳定性,可应用于信息融合、不确定信息决策等领域.     

Quality assessment of speech coding and speech synthesis systems

The concept of speech quality assessment is examined. Quality assessment methodologies for speech waveform coding, source coding, and speech synthesis by rule from the viewpoints of naturalness and intelligibility are reviewed. Both subjective and objective measures are considered     

基于包-全法的红外滤光片光学参数测量方法

为了研究准确性更高的复杂多层膜光学参数测量方法,测量实际镀制红外带通滤光片的光学参数,对红外滤光片研制过程的设计优化与工艺的改进具有重要的指导作用。首先,在研究传统薄膜光学参数光谱测量方法的基础上,提出了包-全法,并研究了该方法的基本思想、物理模型以及优化算法;其次,设计制备了2 000~8 000 nm谱段内膜料单层膜和高透射率、宽截止中波带通红外滤光片,通过对比测量单层膜光学参数反演计算光谱与实测光谱的差异,验证了包-全法测量膜料单层膜光学参数的准确度及有效性,依据测量结果确定了膜料色散关系,甄别了膜层工艺的优劣;最后,采用包-全法与全光谱拟合反演法对红外滤光片的光学参数作了对比测量验证。结果证明:该方法能够准确测量红外滤光片的光学参数,测量结果可用于指导修正设计与工艺之间的匹配性,进而研制了性能更好的红外滤光片。