蒙古语长音频语音文本自动对齐的研究

目前,面向蒙古语的语音识别语音库资源相对稀缺,但存在较多的电视剧、广播等蒙古语音频和对应的文本。该文提出基于语音识别的蒙古语长音频语音文本自动对齐方法,实现蒙古语电视剧语音的自动标注,扩充了蒙古语语音库。在前端处理阶段,使用基于高斯混合模型的语音端点检测技术筛选并删除噪音段;在语音识别阶段,构建基于前向型序列记忆网络的蒙古语声学模型;最后基于向量空间模型,将语音识别得到的假设序列和参考音素序列进行句子级别的动态时间归整算法匹配。实验结果表明,与基于Needleman-Wunsch算法的语音对齐比较,该文提出的蒙古语长音频语音文本自动对齐方法的对齐正确率提升了31.09%。     

基于发音特征的声效相关鲁棒语音识别算法

针对声效(VE)相关的语音识别鲁棒性问题,提出了基于多模型框架的语音识别算法.首先,分析了不同声效模式下语音信号的声学特性以及声效变化对语音识别精度的影响;然后,提出了基于高斯混合模型(GMM)的声效模式检测方法;最后,根据声效检测的结果,训练专门的声学模型用于耳语音识别,而将发音特征与传统的谱特征一起用于其余4种声效模式的语音识别.基于孤立词识别的实验结果显示,采用所提方法后语音识别准确率有了明显的提高:与基线系统相比,所提方法5种声效的平均字错误率降低了26.69%;与声学模型混合语料训练方法相比,平均字错误率降低了14.51%;与最大似然线性回归(MLLR)自适应方法相比,平均字错误率降低了15.30%.实验结果表明:与传统谱特征相比发音特征对于声效变化更具鲁棒性,而多模型框架是解决声效相关的语音识别鲁棒性问题的有效方法.     

基于语音数据库的文语转换系统过程分析

文语转换是中文信息处理中研究的热点,是实现人机语音通信的一项关键技术。文章对实现中文文语转换的整个过程进行了初步分析和研究,给出了基于语音数据库的文语转换方法和实现过程。具体介绍了语音库的建立,分析了文本录入、文本分词、文本正则化、语音标注、韵律处理和语音合成等各个环节处理的内容及技术难点。     

双语法律文本的自动对齐

基于实例的机器翻译系统需要双语句对的支持。为大量获取双语句对,则需要以篇章对齐的双语文本为输入,实现句子的自动对齐。通过分析汉英双语法律文本的特征,提出了法律文本对齐假设。首先识别出法规源文和译文中的结构标识和句子,然后在句子一级对齐法律文本。该方法在150篇汉英法律文本语料上,取得了80.98%的对齐准确率。     

跨模态信息融合的端到端语音翻译

语音翻译旨在将一种语言的语音翻译成另一种语言的语音或文本. 相比于级联式翻译系统, 端到端的语音翻译方法具有时间延迟低、错误累积少和存储空间小等优势, 因此越来越多地受到研究者们的关注. 但是, 端到端的语音翻译方法不仅需要处理较长的语音序列, 提取其中的声学信息, 而且需要学习源语言语音和目标语言文本之间的对齐关系, 从而导致建模困难, 且性能欠佳. 提出一种跨模态信息融合的端到端的语音翻译方法, 该方法将文本机器翻译与语音翻译模型深度结合, 针对语音序列长度与文本序列长度不一致的问题, 通过过滤声学表示中的冗余信息, 使过滤后的声学状态序列长度与对应的文本序列尽可能一致; 针对对齐关系难学习的问题, 采用基于参数共享的方法将文本机器翻译模型嵌入到语音翻译模型中, 并通过多任务训练方法学习源语言语音与目标语言文本之间的对齐关系. 在公开的语音翻译数据集上进行的实验表明, 所提方法可以显著提升语音翻译的性能.     

基于多模态信息融合的语音意图理解研究

为从语音中获取包括字面含义和说话人情绪状态在内的全面意图信息,提出了一种基于多模态信息融合的语音意图理解方法,并对其中的关键词抽取、命令解析、基于文本/韵律特征的情绪状态检测以及多模态信息融合等关键算法进行了设计.该方法从识别文本和语音信号中抽取不同模态的信息并进行融合,能够有效地从语音中获取丰富的意图信息,有助于建立自然的人机交互环境.     

基于发音特征的汉语声调建模方法及其在汉语语音识别中的应用

发音特征表征了语音的发音方式信息,能够辅助传统的韵律特征改善声调建模的精度。在分析汉语声韵母发音特点的基础上,将发音方式划分为19类,并提出利用阶层式多层感知器计算语音信号属于各类的后验概率,作为发音特征。之后,将发音特征与传统的韵律特征一起用于声调建模。实验结果显示,加入发音特征后,在三种不同的建模方法下声调识别的准确率提升约5%。将声调模型融入大词表连续语音识别系统后,汉字错误率有了明显的下降     

基于分类回归树CART的汉语韵律短语边界识别

提出了一种基于分类回归树（Classification And Regression Tree,CART）的汉语韵律短语识别方法。该方法从语音流中提取与韵律短语边界有关的声学特征,从文本中提取短语边界的语言学特征,并将两类特征有机结合构成CART特征集,建立CART决策模型。开放测试结果显示,利用该CART模型在词边界中识别韵律短语边界,其识别准确率平均可达95.91%。     

基于HMM模型的语音单元边界的自动切分

基于隐尔马可夫模型（HMM）的强制对齐方法被用于文语转换系统（TTS）语音单元边界切分.为提高切分准确性,本文对HMM模型的特征选择,模型参数和模型聚类进行优化.实验表明：12维静态Mel频率倒谱系数（MFCC）是最优的语音特征;HMM模型中的状态模型采用单高斯;对于特定说话人的HMM模型,使用分类与衰退树（CART）聚类生成的绑定状态模型个数在3 000左右最优.在英文语音库中音素边界切分的实验中,切分准确率从模型优化前的77.3%提高到85.4%.     

基于语块的英语文语转换系统的韵律生成方法

为预测英语文语转换(Text-to-Speech,TIS)系统中韵律生成模块的韵律边界,通过在中间短语、语调短语和语句后分别插入不同长度的停顿,产生使合成语音具有与真人语音类似的韵律结构.通过采用基于语块的中间短语切分,以中间短语为基本单位,生成一个语调短语边界预测的学习语料库,然后采用转换式学习法进行标注学习,从而实现韵律边界的切分.在对真人语料库进行测试的实验中,标注正确率达到81.32%,通过在学习中增加语调短语音节数和标点符号的约束规则,可进一步提高标注正确率.     

Unsupervised Adaptation of Categorical Prosody Models for Prosody Labeling and Speech Recognition

Automatic speech recognition (ASR) systems rely almost exclusively on short-term segment-level features (MFCCs), while ignoring higher level suprasegmental cues that are characteristic of human speech. However, recent experiments have shown that categorical representations of prosody, such as those based on the Tones and Break Indices (ToBI) annotation standard, can be used to enhance speech recognizers. However, categorical prosody models are severely limited in scope and coverage due to the lack of large corpora annotated with the relevant prosodic symbols (such as pitch accent, word prominence, and boundary tone labels). In this paper, we first present an architecture for augmenting a standard ASR with symbolic prosody. We then discuss two novel, unsupervised adaptation techniques for improving, respectively, the quality of the linguistic and acoustic components of our categorical prosody models. Finally, we implement the augmented ASR by enriching ASR lattices with the adapted categorical prosody models. Our experiments show that the proposed unsupervised adaptation techniques significantly improve the quality of the prosody models; the adapted prosodic language and acoustic models reduce binary pitch accent (presence versus absence) classification error rate by 13.8% and 4.3%, respectively (relative to the seed models) on the Boston University Radio News Corpus, while the prosody-enriched ASR exhibits a 3.1% relative reduction in word error rate (WER) over the baseline system.     

基于拼音约束联合学习的汉语语音识别

当前的语音识别模型在英语、法语等表音文字中已取得很好的效果。然而,汉语是一种典型的表意文字,汉字与语音没有直接的对应关系,但拼音作为汉字读音的标注符号,与汉字存在相互转换的内在联系。因此,在汉语语音识别中利用拼音作为解码时的约束,可以引入一种更接近语音的归纳偏置。该文基于多任务学习框架,提出一种基于拼音约束联合学习的汉语语音识别方法,以端到端的汉字语音识别为主任务,以拼音语音识别为辅助任务,通过共享编码器,同时利用汉字与拼音识别结果作为监督信号,增强编码器对汉语语音的表达能力。实验结果表明,相比基线模型,该文提出的方法取得了更优的识别效果,词错误率降低了2.24%。     

A spoken query system for the agricultural commodity prices and weather information access in Kannada language

In this paper, a spoken query system is demonstrated which can be used to access the latest agricultural commodity prices and weather information in Kannada language using mobile phone. The spoken query system consists of Automatic Speech Recognition (ASR) models, Interactive Voice Response System (IVRS) call flow, Agricultural Marketing Network (AGMARKNET) and India Meteorological Department (IMD) databases. The ASR models are developed by using the Kaldi speech recognition toolkit. The task specific speech data is collected from the different dialect regions of Karnataka (a state in India speaks Kannada language) to develop ASR models. The web crawler is used to get the commodity price and weather information from AGMARKNET and IMD websites. The postgresql database management system is used to manage the crawled data. The 80 and 20% of validated speech data is used for system training and testing respectively. The accuracy and Word Error Rate (WER) of ASR models are highlighted and end to end spoken query system is developed for Kannada language.     

基于韵律特征和语法信息的韵律边界检测模型

韵律短语边界的自动检测,对语音合成中语料库的韵律标注以及语音识别中韵律短语的自动划分都有重要意义。本文通过对影响韵律短语边界的声学、韵律等参量的分析,得到和韵律短语边界关联性较大的一组声学特征参数、韵律环境参数和语法信息;同时引入语音合成中的韵律预测思想,在假定所有音节边界均为非韵律短语边界时,预测每个音节的基频。最后使用决策树模型,将音节边界处的韵律环境信息、语法信息以及预测结果作为决策树的输入,利用决策树综合判定当前音节边界是否为韵律短语的边界。实验表明,这种方法对于基于确定性文本(text-dependent)的语音韵律短语边界的检测,具有较好效果,同时可以显著提高语音合成中语料库的标注效率和标注结果的一致性。     

Robust Arabic speech recognition in noisy environments using prosodic features and formant

This paper investigates the contribution of formants and prosodic features such as pitch and energy in Arabic speech recognition under real-life conditions. Our speech recognition system based on Hidden Markov Models (HMMs) is implemented using the HTK Toolkit. The front-end of the system combines features based on conventional Mel-Frequency Cepstral Coefficient (MFFC), prosodic information and formants. The experiments are performed on the ARADIGIT corpus which is a database of Arabic spoken words. The obtained results show that the resulting multivariate feature vectors, in noisy environment, lead to a significant improvement, up to 27%, in word accuracy relative the word accuracy obtained from the state-of-the-art MFCC-based system.     

Comparing ANN to HMM in implementing limited Arabic vocabulary ASR systems

In this paper we investigated Artificial Neural Networks (ANN) based Automatic Speech Recognition (ASR) by using limited Arabic vocabulary corpora. These limited Arabic vocabulary subsets are digits and vowels carried by specific carrier words. In addition to this, Hidden Markov Model (HMM) based ASR systems are designed and compared to two ANN based systems, namely Multilayer Perceptron (MLP) and recurrent architectures, by using the same corpora. All systems are isolated word speech recognizers. The ANN based recognition system achieved 99.5% correct digit recognition. On the other hand, the HMM based recognition system achieved 98.1% correct digit recognition. With vowels carrier words, the MLP and recurrent ANN based recognition systems achieved 92.13% and 98.06, respectively, correct vowel recognition; but the HMM based recognition system achieved 91.6% correct vowel recognition.     

基于韵律特征辅助的端到端语音识别方法

针对传统的语音识别系统采用数据驱动并利用语言模型来决策最优的解码路径,导致在部分场景下的解码结果存在明显的音对字错的问题,提出一种基于韵律特征辅助的端到端语音识别方法,利用语音中的韵律信息辅助增强正确汉字组合在语言模型中的概率。在基于注意力机制的编码-解码语音识别框架的基础上,首先利用注意力机制的系数分布提取发音间隔、发音能量等韵律特征;然后将韵律特征与解码端结合,从而显著提升了发音相同或相近、语义歧义情况下的语音识别准确率。实验结果表明,该方法在1 000 h及10 000 h级别的语音识别任务上分别较端到端语音识别基线方法在准确率上相对提升了5.2%和5.0%,进一步改善了语音识别结果的可懂度。     

Level of interest sensing in spoken dialog using decision-level fusion of acoustic and lexical evidence

Automatic detection of a user's interest in spoken dialog plays an important role in many applications, such as tutoring systems and customer service systems. In this study, we propose a decision-level fusion approach using acoustic and lexical information to accurately sense a user's interest at the utterance level. Our system consists of three parts: acoustic/prosodic model, lexical model, and a model that combines their decisions for the final output. We use two different regression algorithms to complement each other for the acoustic model. For lexical information, in addition to the bag-of-words model, we propose new features including a level-of-interest value for each word, length information using the number of words, estimated speaking rate, silence in the utterance, and similarity with other utterances. We also investigate the effectiveness of using more automatic speech recognition (ASR) hypotheses (n-best lists) to extract lexical features. The outputs from the acoustic and lexical models are combined at the decision level. Our experiments show that combining acoustic evidence with lexical information improves level-of-interest detection performance, even when lexical features are extracted from ASR output with high word error rate.     

Integration of multiple acoustic and language models for improved Hindi speech recognition system

Despite the significant progress of automatic speech recognition (ASR) in the past three decades, it could not gain the level of human performance, particularly in the adverse conditions. To improve the performance of ASR, various approaches have been studied, which differ in feature extraction method, classification method, and training algorithms. Different approaches often utilize complementary information; therefore, to use their combination can be a better option. In this paper, we have proposed a novel approach to use the best characteristics of conventional, hybrid and segmental HMM by integrating them with the help of ROVER system combination technique. In the proposed framework, three different recognizers are created and combined, each having its own feature set and classification technique. For design and development of the complete system, three separate acoustic models are used with three different feature sets and two language models. Experimental result shows that word error rate (WER) can be reduced about 4% using the proposed technique as compared to conventional methods. Various modules are implemented and tested for Hindi Language ASR, in typical field conditions as well as in noisy environment.     

Prosody dependent speech recognition on radio news corpus of American English

Does prosody help word recognition? This paper proposes a novel probabilistic framework in which word and phoneme are dependent on prosody in a way that reduces word error rates (WER) relative to a prosody-independent recognizer with comparable parameter count. In the proposed prosody-dependent speech recognizer, word and phoneme models are conditioned on two important prosodic variables: the intonational phrase boundary and the pitch accent. An information-theoretic analysis is provided to show that prosody dependent acoustic and language modeling can increase the mutual information between the true word hypothesis and the acoustic observation by exciting the interaction between prosody dependent acoustic model and prosody dependent language model. Empirically, results indicate that the influence of these prosodic variables on allophonic models are mainly restricted to a small subset of distributions: the duration PDFs (modeled using an explicit duration hidden Markov model or EDHMM) and the acoustic-prosodic observation PDFs (normalized pitch frequency). Influence of prosody on cepstral features is limited to a subset of phonemes: for example, vowels may be influenced by both accent and phrase position, but phrase-initial and phrase-final consonants are independent of accent. Leveraging these results, effective prosody dependent allophonic models are built with minimal increase in parameter count. These prosody dependent speech recognizers are able to reduce word error rates by up to 11% relative to prosody independent recognizers with comparable parameter count, in experiments based on the prosodically-transcribed Boston Radio News corpus.