维吾尔语情感语音韵律转换研究

面向维吾尔语情感语音转换,提出一种韵律建模转换方法。该方法结合了维吾尔语韵律特点及语言特点,首次利用离散余弦变换（DCT）分别参数化维吾尔语音节和韵律短语的情感基频。采用高斯混合模型（GMM）训练中性-情感基频联合特征,同时合成中性语速情感语音和情感语速情感语音,主观评测结果显示情感语速更有助于表达情感效果。主客观实验结果显示转换方法可有效进行维吾尔语情感韵律转换,三种情感下,音节和韵律短语的结果均达到75%以上,韵律短语的转换效果要稍优于音节。     

基于PAD三维情绪模型的情感语音韵律转换

提出了一种基于PAD三维情绪模型的情感语音韵律转换方法。选取了11种典型情感,设计了文本语料,录制了语音语料,利用心理学的方法标注了语音语料的PAD值,利用五度字调模型对情感语音音节的基频曲线建模。在此基础上,利用广义回归神经网络(Generalized Regression Neural Network,GRNN)构建了一个情感语音韵律转换模型,根据情感的PAD值和语句的语境参数预测情感语音的韵律特征,并采用STRAIGHT算法实现了情感语音的转换。主观评测结果表明,提出的方法转换得到的11种情感语音,其平均EMOS(Emotional Mean Opinion Score)得分为3.6,能够表现出相应的情感。     

情感语音合成中韵律参数的基频研究

在语音合成技术的研究中,情感语音合成是当前研究的热点.在众多研究因素中,建立恰当的韵律模型和选取好的韵律参数是研究的关键,它们描述的正确与否,直接影响到情感语音合成的输出效果.为了攻克提高情感语音自然度这一难点,对影响情感语音合成技术韵律参数进行了分析,建立了基于关联规则的情感语音韵律基频模型.本文通过研究关联规则、改进数据挖掘Apriori算法并由此来获得韵律参数中基频变化规则,并为情感语音合成的选音提供指导和帮助.     

基于Tacotron模型和韵律修正的情感语音合成方法

语音合成技术日趋成熟,为了提高合成情感语音的质量,提出了一种端到端情感语音合成与韵律修正相结合的方法。在Tacotron模型合成的情感语音基础上,进行韵律参数的修改,提高合成系统的情感表达力。首先使用大型中性语料库训练Tacotron模型,再使用小型情感语料库训练,合成出具有情感的语音。然后采用Praat声学分析工具对语料库中的情感语音韵律特征进行分析并总结不同情感状态下的参数规律,最后借助该规律,对Tacotron合成的相应情感语音的基频、时长和能量进行修正,使情感表达更为精确。客观情感识别实验和主观评价的结果表明,该方法能够合成较为自然且表现力更加丰富的情感语音。     

基于韵律特征参数的情感语音合成算法研究

为了合成更为自然的情感语音,提出了基于语音信号声学韵律参数及时域基音同步叠加算法的情感语音合成系统.实验通过对情感语音数据库中生气、无聊、高兴和悲伤4种情感的韵律参数分析,建立4种情感模板,采用波形拼接语音合成技术,运用时域基音同步叠加算法合成含有目标感情色彩的语音信号.实验结果表明,运用波形拼接算法,调节自然状态下语音信号的韵律特征参数,可合成较理想的情感语音.合成的目标情感语音具有明显的感情色彩,其主观情感类别判别正确率较高.     

基于广义回归神经网络的情感语音韵律特征预测

韵律特征是语音信号中情感信息的主要表征之一。为了更好地进行情感语音合成的研究,本文通过提取普通话情感语音的韵律特征进行分析,采用广义回归神经网络构建了一个情感语音韵律特征预测模型,并根据所提取的测试集数据文本语境信息进行韵律特征预测,实验获得了相应的结果。实验结果表明,情感语音韵律特征预测效果较好。     

情感语音识别技术在军用智能机器人中的应用

提出了一种基于隐马尔可夫模型(HMM)与人工神经网络(ANN)相结合的情感语音识别系统的实现方法.并从情感语音资料的获取、情感语音特征的提取及情感语音识别等方面阐明了情感语音识别系统的建立过程.该系统实现了情感语音特征参数的提取、情感语音模型参数的训练及对录入的情感语音进行识别等功能.研究结果表明了该系统识别效果良好.     

基于ASGRU-CNN时空双通道的语音情感识别

语音情感识别是实现人机交互的关键,如何提升语音情感识别的准确率以及更有效地提取具有情感代表性的特征是语音情感识别所面临的问题之一。针对以上问题,构建了一种包含空间特征提取模块和时序特征提取模块的双通道时空语音情感识别模型ASGRU-CNN。模型总体框架由两条并行分支组成：第一分支为空间特征提取模块,由三维卷积、二维卷积及池化操作共同构成级联结构;第二分支为时序特征提取模块,由切片循环神经网络内嵌门控循环单元及注意力机制构成。模型以韵律特征及谱特征的融合特征作为输入特征,经过双分支处理后,进入全连接层进行语音情感分类。在CASIA与EMO-DB数据库上进行相关实验,并通过数据扩充增加训练样本,与其它语音情感识别模型实验结果相比,所提出的模型具有较好的鲁棒性和泛化性。     

面向数据增强的多种语音情感分类算法研究

目前语音情感识别存在语音样本不足、提取的特征数据量大和无关特征多使得识别率不高的问题。针对语音样本不足的情况,在预处理阶段提出了时频域的数据增强方法,对原有的数据库进行扩充;根据传统算法中提取的特征数据量大且与情感无关的特征多的现状,提取了1 582维的情感特征和10组低级描述特征。分别在支持向量机、随机森林和K最邻近3种机器学习算法上做了对比实验。实验证明:支持向量机的平均识别率比较好。在所提取的10组特征组中,LogMelFreqBand特征在3种算法上的精确度分别为74.63%、64.93%和66.42%;而pcm_fftMag_mfcc特征的精确度分别为84.33%、73.13%和58.21%。     

基于异构并行神经网络的语音情感识别

提取能表征语音情感的特征并构建具有较强鲁棒性和泛化性的声学模型是语音情感识别系统的核心。面向语音情感识别构建基于注意力机制的异构并行卷积神经网络模型AHPCL,采用长短时记忆网络提取语音情感的时间序列特征,使用卷积操作提取语音空间谱特征,通过将时间信息和空间信息相结合共同表征语音情感,提高预测结果的准确率。利用注意力机制,根据不同时间序列特征对语音情感的贡献程度分配权重,实现从大量特征信息中选择出更能表征语音情感的时间序列。在CASIA、EMODB、SAVEE等3个语音情感数据库上提取音高、过零率、梅尔频率倒谱系数等低级描述符特征,并计算这些低级描述符特征的高级统计函数共得到219维的特征作为输入进行实验验证。结果表明,AHPCL模型在3个语音情感数据库上分别取得了86.02%、84.03%、64.06%的未加权平均召回率,相比LeNet、DNN-ELM和TSFFCNN基线模型具有更强的鲁棒性和泛化性。     

Emotion Recognition with Capsule Neural Network

For human-machine communication to be as effective as human-to-human communication, research on speech emotion recognition is essential. Among the models and the classifiers used to recognize emotions, neural networks appear to be promising due to the network’s ability to learn and the diversity in configuration. Following the convolutional neural network, a capsule neural network (CapsNet) with inputs and outputs that are not scalar quantities but vectors allows the network to determine the part-whole relationships that are specific 6 for an object. This paper performs speech emotion recognition based on CapsNet. The corpora for speech emotion recognition have been augmented by adding white noise and changing voices. The feature parameters of the recognition system input are mel spectrum images along with the characteristics of the sound source, vocal tract and prosody. For the German emotional corpus EMO-DB, the average accuracy score for 4 emotions, neutral, boredom, anger and happiness, is 99.69%. For Vietnamese emotional corpus BKEmo, this score is 94.23% for 4 emotions, neutral, sadness, anger and happiness. The accuracy score is highest when combining all the above feature parameters, and this score increases significantly when combining mel spectrum images with the features directly related to the fundamental frequency.     

Prosody modification for speech recognition in emotionally mismatched conditions

A degradation in the performance of automatic speech recognition systems (ASR) is observed in mismatched training and testing conditions. One of the reasons for this degradation is due to the presence of emotions in the speech. The main objective of this work is to improve the performance of ASR in the presence of emotional conditions using prosody modification. The influence of different emotions on the prosody parameters is exploited in this work. Emotion conversion methods are employed to generate the word level non-uniform prosody modified speech. Modification factors for prosodic components such as pitch, duration and energy are used. The prosody modification is done in two ways. Firstly, emotion conversion is done at the testing stage to generate the neutral speech from the emotional speech. Secondly, the ASR is trained with the generated emotional speech from the neutral speech. In this work, the presence of emotions in speech is studied for the Telugu ASR systems. A new database of IIIT-H Telugu speech corpus is collected to build the large vocabulary neutral Telugu speech ASR system. The emotional speech samples from IITKGP-SESC Telugu corpus are used for testing it. The emotions of anger, happiness and compassion are considered during the evaluation. An improvement in the performance of ASR systems is observed in the prosody modified speech.     

基于非线性数据融合的设备多阶段寿命预测

为解决设备监测数据具有维数高、非线性且退化过程中存在多阶段的问题,提出了一种基于非线性数据融合和多阶段退化的设备寿命预测模型.首先,利用神经网络理论中的自编码器对表征设备退化的多维参数进行了融合,构建出设备的退化指示量;然后,利用CUSUM算法提取出设备退化过程中的分段点;最后,构建了多阶段维纳退化模型,从而实现对设备寿命的预测.利用航空发动机状态监测数据对所提模型进行了验证,剩余寿命预测的平均误差为0.254 5,低于传统的基于线性数据融合方法和基于单阶段维纳过程退化模型的寿命预测方法.结果证明,基于非线性数据融合的多阶段退化模型具有很好的鲁棒性,对设备的寿命预测更加精准.     

基于Spark的快速短文本数据流分类方法

微博、脸书等社交网络平台涌现的短文本数据流具有海量、高维稀疏、快速可变等特性，使得短文本数据流分类面临着巨大挑战。已有的短文本数据流分类方法难以有效地解决特征高维稀疏问题，并且在处理海量数据流时时间代价较高。基于此，提出一种基于Spark的分布式快速短文本数据流分类方法。一方面，利用外部语料库构建Word2vec词向量模型解决了短文本的高维稀疏问题，并构建扩展词向量库以适应文本的快速可变性，提出一种LR分类器集成模型用于短文本数据流分类，该分类器使用一种FTRL方法实现模型参数的在线更新，并引入时间因子加权机制以适应概念漂移环境；另一方面，所提方法的使用分布式处理提高了海量短文本数据流的处理效率。在3个真实短文本数据流上的实验表明：所提方法在提高分类精度的同时，降低了时间消耗。     

Prosody conversion from neutral speech to emotional speech

Emotion is an important element in expressive speech synthesis. Unlike traditional discrete emotion simulations, this paper attempts to synthesize emotional speech by using "strong", "medium", and "weak" classifications. This paper tests different models, a linear modification model (LMM), a Gaussian mixture model (GMM), and a classification and regression tree (CART) model. The linear modification model makes direct modification of sentence F0 contours and syllabic durations from acoustic distributions of emotional speech, such as, F0 topline, F0 baseline, durations, and intensities. Further analysis shows that emotional speech is also related to stress and linguistic information. Unlike the linear modification method, the GMM and CART models try to map the subtle prosody distributions between neutral and emotional speech. While the GMM just uses the features, the CART model integrates linguistic features into the mapping. A pitch target model which is optimized to describe Mandarin F0 contours is also introduced. For all conversion methods, a deviation of perceived expressiveness (DPE) measure is created to evaluate the expressiveness of the output speech. The results show that the LMM gives the worst results among the three methods. The GMM method is more suitable for a small training set, while the CART method gives the better emotional speech output if trained with a large context-balanced corpus. The methods discussed in this paper indicate ways to generate emotional speech in speech synthesis. The objective and subjective evaluation processes are also analyzed. These results support the use of a neutral semantic content text in databases for emotional speech synthesis.     

基于OCC模型和贝叶斯网络的情绪句分类方法

情绪句分类是情绪分析研究领域的核心问题之一,旨在解决情绪句类别的自动判断问题。传统基于情绪认知模型(OCC模型)的情绪句分类方法大多依赖词典和规则,在文本信息缺失的情况下分类精度不高。文中提出基于OCC模型和贝叶斯网络的情绪句分类方法,通过分析OCC模型的情绪生成规则,提取情绪评估变量并结合情绪句中含有的表情符号特征构建情绪分类贝叶斯网络;通过概率推理,可以实现句子级文本的情绪分类,并减小句中信息缺失所带来的影响。与NLPCC2014中文微博情绪分析评测的子任务情绪句分类评测结果的对比表明,所提方法具有有效性。     

An objective and subjective study of the role of semantics and prosodic features in building corpora for emotional TTS

Building a text corpus suitable to be used in corpus-based speech synthesis is a time-consuming process that usually requires some human intervention to select the desired phonetic content and the necessary variety of prosodic contexts. If an emotional text-to-speech (TTS) system is desired, the complexity of the corpus generation process increases. This paper presents a study aiming to validate or reject the use of a semantically neutral text corpus for the recording of both neutral and emotional (acted) speech. The use of this kind of texts would eliminate the need to include semantically emotional texts into the corpus. The study has been performed for Basque language. It has been made by performing subjective and objective comparisons between the prosodic characteristics of recorded emotional speech using both semantically neutral and emotional texts. At the same time, the performed experiments allow for an evaluation of the capability of prosody to carry emotional information in Basque language. Prosody manipulation is the most common processing tool used in concatenative TTS. Experiments of automatic recognition of the emotions considered in this paper (the "Big Six emotions") show that prosody is an important emotional indicator, but cannot be the only manipulated parameter in an emotional TTS system-at least not for all the emotions. Resynthesis experiments transferring prosody from emotional to neutral speech have also been performed. They corroborate the results and support the use of a neutral-semantic-content text in databases for emotional speech synthesis.     

手语到情感语音的转换

为了解决语言障碍者与健康人之间的交流障碍问题,提出了一种基于神经网络的手语到情感语音转换方法。首先,建立了手势语料库、人脸表情语料库和情感语音语料库;然后利用深度卷积神经网络实现手势识别和人脸表情识别,并以普通话声韵母为合成单元,训练基于说话人自适应的深度神经网络情感语音声学模型和基于说话人自适应的混合长短时记忆网络情感语音声学模型;最后将手势语义的上下文相关标注和人脸表情对应的情感标签输入情感语音合成模型,合成出对应的情感语音。实验结果表明,该方法手势识别率和人脸表情识别率分别达到了95.86%和92.42%,合成的情感语音EMOS得分为4.15,合成的情感语音具有较高的情感表达程度,可用于语言障碍者与健康人之间正常交流。     

改进的CBOW情感信息获取研究

大数据时代,文本的情感倾向对于文本潜在价值挖掘具有重要意义,然而人工方法很难有效挖掘网络上评论文本的潜在价值,随着计算机技术的快速发展,这一问题得到了有效解决。在文本情感分析中,获取词语的情感信息对于情感分析至关重要,词向量方法一般仅对词语的语法语义进行建模,但是忽略了词语的情感信息,无法更好地进行情感分析。通过TF-IDF算法模型获得赋权矩阵,构建停用词表,同时根据赋权矩阵生成Huffman树作为改进的CBOW算法的输入,引入情感词典生成情感标签辅助词向量生成,使词向量具有情感信息。实验结果表明,提出的方法对评论文本中获得的词向量能够较好地表达情感信息,情感分类结果优于传统模型。因此,该模型在评论文本情感分析中可以有效提升文本情感分类效果。