Electrophysiological dynamics of Chinese phonology during visual word recognition in Chinese-English bilinguals

Silent word reading leads to the activation of orthographic (spelling), meaning, as well as phonological (sound) information. For bilinguals, native language information can also be activated automatically when they read words in their second language. For example, when Chinese-English bilinguals read words in their second language (English), the phonology of the Chinese translations is automatically activated. Chinese phonology, however, consists of consonants and vowels (segmental) and tonal information. To what extent these two aspects of Chinese phonology are activated is yet unclear. Here, we used behavioural measures, event-related potentials and oscillatory EEG to investigate Chinese segmental and tonal activation during word recognition. Evidence of Chinese segmental activation was found when bilinguals read English words (faster responses, reduced N400, gamma-band power reduction) and when they read Chinese words (increased LPC, gamma-band power reduction). In contrast, evidence for Chinese tonal activation was only found when bilinguals read Chinese words (gamma-band power increase). Together, our converging behavioural and electrophysiological evidence indicates that Chinese segmental information is activated during English word reading, whereas both segmental and tonal information are activated during Chinese word reading. Importantly, gamma-band oscillations are modulated differently by tonal and segmental activation, suggesting independent processing of Chinese tones and segments

Unifying Amplitude and Phase Analysis: A Compositional Data Approach to Functional Multivariate Mixed-Effects Modeling of Mandarin Chinese

Mandarin Chinese is characterized by being a tonal language; the pitch (or $F_0$) of its utterances carries considerable linguistic information. However, speech samples from different individuals are subject to changes in amplitude and phase which must be accounted for in any analysis which attempts to provide a linguistically meaningful description of the language. A joint model for amplitude, phase and duration is presented which combines elements from Functional Data Analysis, Compositional Data Analysis and Linear Mixed Effects Models. By decomposing functions via a functional principal component analysis, and connecting registration functions to compositional data analysis, a joint multivariate mixed effect model can be formulated which gives insights into the relationship between the different modes of variation as well as their dependence on linguistic and non-linguistic covariates. The model is applied to the COSPRO-1 data set, a comprehensive database of spoken Taiwanese Mandarin, containing approximately 50 thousand phonetically diverse sample $F_0$ contours (syllables), and reveals that phonetic information is jointly carried by both amplitude and phase variation.Comment: 49 pages, 13 figures, small changes to discussio

Improving the Speech Intelligibility By Cochlear Implant Users

In this thesis, we focus on improving the intelligibility of speech for cochlear implants (CI) users. As an auditory prosthetic device, CI can restore hearing sensations for most patients with profound hearing loss in both ears in a quiet background. However, CI users still have serious problems in understanding speech in noisy and reverberant environments. Also, bandwidth limitation, missing temporal fine structures, and reduced spectral resolution due to a limited number of electrodes are other factors that raise the difficulty of hearing in noisy conditions for CI users, regardless of the type of noise. To mitigate these difficulties for CI listener, we investigate several contributing factors such as the effects of low harmonics on tone identification in natural and vocoded speech, the contribution of matched envelope dynamic range to the binaural benefits and contribution of low-frequency harmonics to tone identification in quiet and six-talker babble background. These results revealed several promising methods for improving speech intelligibility for CI patients. In addition, we investigate the benefits of voice conversion in improving speech intelligibility for CI users, which was motivated by an earlier study showing that familiarity with a talker’s voice can improve understanding of the conversation. Research has shown that when adults are familiar with someone’s voice, they can more accurately – and even more quickly – process and understand what the person is saying. This theory identified as the “familiar talker advantage” was our motivation to examine its effect on CI patients using voice conversion technique. In the present research, we propose a new method based on multi-channel voice conversion to improve the intelligibility of transformed speeches for CI patients

Tone classification of syllable -segmented Thai speech based on multilayer perceptron

Thai is a monosyllabic and tonal language. Thai makes use of tone to convey lexical information about the meaning of a syllable. Thai has five distinctive tones and each tone is well represented by a single F0 contour pattern. In general, a Thai syllable with a different tone has a different lexical meaning. Thus, to completely recognize a spoken Thai syllable, a speech recognition system has not only to recognize a base syllable but also to correctly identify a tone. Hence, tone classification of Thai speech is an essential part of a Thai speech recognition system.;In this study, a tone classification of syllable-segmented Thai speech which incorporates the effects of tonal coarticulation, stress and intonation was developed. Automatic syllable segmentation, which performs the segmentation on the training and test utterances into syllable units, was also developed. The acoustical features including fundamental frequency (F0), duration, and energy extracted from the processing syllable and neighboring syllables were used as the main discriminating features. A multilayer perceptron (MLP) trained by backpropagation method was employed to classify these features. The proposed system was evaluated on 920 test utterances spoken by five male and three female Thai speakers who also uttered the training speech. The proposed system achieved an average accuracy rate of 91.36%

Incorporating pitch features for tone modeling in automatic recognition of Mandarin Chinese

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 53-56).Tone plays a fundamental role in Mandarin Chinese, as it plays a lexical role in determining the meanings of words in spoken Mandarin. For example, these two sentences ... (I like horses) and ... (I like to scold) differ only in the tone carried by the last syllable. Thus, the inclusion of tone-related information through analysis of pitch data should improve the performance of automatic speech recognition (ASR) systems on Mandarin Chinese. The focus of this thesis is to improve the performance of a non-tonal automatic speech recognition (ASR) system on a Mandarin Chinese corpus by implementing modifications to the system code to incorporate pitch features. We compile and format a Mandarin Chinese broadcast new corpus for use with the ASR system, and implement a pitch feature extraction algorithm. Additionally, we investigate two algorithms for incorporating pitch features in Mandarin Chinese speech recognition. Firstly, we build and test a baseline tonal ASR system with embedded tone modeling by concatenating the cepstral and pitch feature vectors for use as the input to our phonetic model (a Hidden Markov Model, or HMM). We find that our embedded tone modeling algorithm does improve performance on Mandarin Chinese, showing that including tonal information is in fact contributive for Mandarin Chinese speech recognition. Secondly, we implement and test the effectiveness of HMM-based multistream models.by Karen Lingyun Chu.M.Eng

Prosody analysis and modeling for Cantonese text-to-speech.

Li Yu Jia.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references.Abstracts in English and Chinese.Chapter Chapter 1 --- Introduction --- p.1Chapter 1.1. --- TTS Technology --- p.1Chapter 1.2. --- Prosody --- p.2Chapter 1.2.1. --- What is Prosody --- p.2Chapter 1.2.2. --- Prosody from Different Perspectives --- p.3Chapter 1.2.3. --- Acoustical Parameters of Prosody --- p.3Chapter 1.2.4. --- Prosody in TTS --- p.5Chapter 1.2.4.1 --- Analysis --- p.5Chapter 1.2.4.2 --- Modeling --- p.6Chapter 1.2.4.3 --- Evaluation --- p.6Chapter 1.3. --- Thesis Objectives --- p.7Chapter 1.4. --- Thesis Outline --- p.7Reference --- p.8Chapter Chapter 2 --- Cantonese --- p.9Chapter 2.1. --- The Cantonese Dialect --- p.9Chapter 2.1.1. --- Phonology --- p.10Chapter 2.1.1.1 --- Initial --- p.11Chapter 2.1.1.2 --- Final --- p.12Chapter 2.1.1.3 --- Tone --- p.13Chapter 2.1.2. --- Phonological Constraints --- p.14Chapter 2.2. --- Tones in Cantonese --- p.15Chapter 2.2.1. --- Tone System --- p.15Chapter 2.2.2. --- Linguistic Significance --- p.18Chapter 2.2.3. --- Acoustical Realization --- p.18Chapter 2.3. --- Prosodic Variation in Continuous Cantonese Speech --- p.20Chapter 2.4. --- Cantonese Speech Corpus - CUProsody --- p.21Reference --- p.23Chapter Chapter 3 --- F0 Normalization --- p.25Chapter 3.1. --- F0 in Speech Production --- p.25Chapter 3.2. --- F0 Extraction --- p.27Chapter 3.3. --- Duration-normalized Tone Contour --- p.29Chapter 3.4. --- F0 Normalization --- p.30Chapter 3.4.1. --- Necessity and Motivation --- p.30Chapter 3.4.2. --- F0 Normalization --- p.33Chapter 3.4.2.1 --- Methodology --- p.33Chapter 3.4.2.2 --- Assumptions --- p.34Chapter 3.4.2.3 --- Estimation of Relative Tone Ratios --- p.35Chapter 3.4.2.4 --- Derivation of Phrase Curve --- p.37Chapter 3.4.2.5 --- Normalization of Absolute FO Values --- p.39Chapter 3.4.3. --- Experiments and Discussion --- p.39Chapter 3.5. --- Conclusions --- p.44Reference --- p.45Chapter Chapter 4 --- Acoustical FO Analysis --- p.48Chapter 4.1. --- Methodology of FO Analysis --- p.48Chapter 4.1.1. --- Analysis-by-Synthesis --- p.48Chapter 4.1.2. --- Acoustical Analysis --- p.51Chapter 4.2. --- Acoustical FO Analysis for Cantonese --- p.52Chapter 4.2.1. --- Analysis of Phrase Curves --- p.52Chapter 4.2.2. --- Analysis of Tone Contours --- p.55Chapter 4.2.2.1 --- Context-independent Single-tone Contours --- p.56Chapter 4.2.2.2 --- Contextual Variation --- p.58Chapter 4.2.2.3 --- Co-articulated Tone Contours of Disyllabic Word --- p.59Chapter 4.2.2.4 --- Cross-word Contours --- p.62Chapter 4.2.2.5 --- Phrase-initial Tone Contours --- p.65Chapter 4.3. --- Summary --- p.66Reference --- p.67Chapter Chapter5 --- Prosody Modeling for Cantonese Text-to-Speech --- p.70Chapter 5.1. --- Parametric Model and Non-parametric Model --- p.70Chapter 5.2. --- Cantonese Text-to-Speech: Baseline System --- p.72Chapter 5.2.1. --- Sub-syllable Unit --- p.72Chapter 5.2.2. --- Text Analysis Module --- p.73Chapter 5.2.3. --- Acoustical Synthesis --- p.74Chapter 5.2.4. --- Prosody Module --- p.74Chapter 5.3. --- Enhanced Prosody Model --- p.74Chapter 5.3.1. --- Modeling Tone Contours --- p.75Chapter 5.3.1.1 --- Word-level FO Contours --- p.76Chapter 5.3.1.2 --- Phrase-initial Tone Contours --- p.77Chapter 5.3.1.3 --- Tone Contours at Word Boundary --- p.78Chapter 5.3.2. --- Modeling Phrase Curves --- p.79Chapter 5.3.3. --- Generation of Continuous FO Contours --- p.81Chapter 5.4. --- Summary --- p.81Reference --- p.82Chapter Chapter 6 --- Performance Evaluation --- p.83Chapter 6.1. --- Introduction to Perceptual Test --- p.83Chapter 6.1.1. --- Aspects of Evaluation --- p.84Chapter 6.1.2. --- Methods of Judgment Test --- p.84Chapter 6.1.3. --- Problems in Perceptual Test --- p.85Chapter 6.2. --- Perceptual Tests for Cantonese TTS --- p.86Chapter 6.2.1. --- Intelligibility Tests --- p.86Chapter 6.2.1.1 --- Method --- p.86Chapter 6.2.1.2 --- Results --- p.88Chapter 6.2.1.3 --- Analysis --- p.89Chapter 6.2.2. --- Naturalness Tests --- p.90Chapter 6.2.2.1 --- Word-level --- p.90Chapter 6.2.2.1.1 --- Method --- p.90Chapter 6.2.2.1.2 --- Results --- p.91Chapter 6.2.3.1.3 --- Analysis --- p.91Chapter 6.2.2.2 --- Sentence-level --- p.92Chapter 6.2.2.2.1 --- Method --- p.92Chapter 6.2.2.2.2 --- Results --- p.93Chapter 6.2.2.2.3 --- Analysis --- p.94Chapter 6.3. --- Conclusions --- p.95Chapter 6.4. --- Summary --- p.95Reference --- p.96Chapter Chapter 7 --- Conclusions and Future Work --- p.97Chapter 7.1. --- Conclusions --- p.97Chapter 7.2. --- Suggested Future Work --- p.99Appendix --- p.100Appendix 1 Linear Regression --- p.100Appendix 2 36 Templates of Cross-word Contours --- p.101Appendix 3 Word List for Word-level Tests --- p.102Appendix 4 Syllable Occurrence in Word List of Intelligibility Test --- p.108Appendix 5 Wrongly Identified Word List --- p.112Appendix 6 Confusion Matrix --- p.115Appendix 7 Unintelligible Word List --- p.117Appendix 8 Noisy Word List --- p.119Appendix 9 Sentence List for Naturalness Test --- p.12