NDLI: Synthesis of Spontaneous Speech With Syllable Contraction Using State-Based Context-Dependent Voice Transformation

Content Provider	IEEE Xplore Digital Library
Author	Chung-Hsien Wu Yi-Chin Huang Chung-Han Lee Jun-Cheng Guo
Copyright Year	2014
Abstract	Pronunciation normally varies in spontaneous speech, and is an integral aspect of spontaneous expression. This study describes a voice transformation-based approach to generating spontaneous speech with syllable contractions for Hidden Markov Model (HMM)-based speech synthesis. A multi-dimensional linear regression model is adopted as the context-dependent, state-based transformation function to convert the feature sequence of read speech to that of spontaneous speech with syllable contraction. With insufficient number of training data, the obtained transformation functions are categorized using a decision tree based on linguistic and articulatory features for better and efficient selection of suitable transformation functions. Furthermore, to cope with the problem of small parallel corpus, cross-validation of trained transformation function is performed to ensure correct transformation functions are obtained and prevent over-fitting. Consequently, pronunciation variations of syllable contraction for the trained and the unseen syllable-contracted words are generated from the transformation function retrieved from the decision tree using linguistic and articulatory features. Objective and subjective tests were used to evaluate the performance of the proposed approach. Evaluation results demonstrate that the proposed transformation function substantially improves apparent spontaneity of the synthesized speech compared to the conventional methods.
Starting Page	585
Ending Page	595
Page Count	11
File Size	2279424
File Format	PDF
ISSN	23299290
Volume Number	22
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Speech Hidden Markov models Vectors Speech synthesis Decision trees Pragmatics transformation function Pronunciation variation speech synthesis
Content Type	Text
Resource Type	Article
Subject	Acoustics and Ultrasonics Signal Processing Instrumentation Speech and Hearing Electrical and Electronic Engineering Computational Mathematics Media Technology

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Synthesis of Spontaneous Speech With Syllable Contraction Using State-Based Context-Dependent Voice Transformation

Characterizing Phonetic Transformations and Acoustic Differences Across English Dialects

Syllable: A self-contained unit to model pronunciation variation

Incremental syllable-context phonetic vocoding

HMM-Based Speech Segmentation: Improvements of Fully Automatic Approaches

Pronunciation variation generation for spontaneous speech synthesis using state-based voice transformation

Informative dialect recognition using context-dependent pronunciation modeling

A Deep Generative Architecture for Postfiltering in Statistical Parametric Speech Synthesis

Automatic Speech Recognition for Mixed Dialect Utterances by Mixing Dialect Language Models

Synthesis of Spontaneous Speech With Syllable Contraction Using State-Based Context-Dependent Voice Transformation

Similar Documents

Synthesis of Spontaneous Speech With Syllable Contraction Using State-Based Context-Dependent Voice Transformation

Characterizing Phonetic Transformations and Acoustic Differences Across English Dialects

Syllable: A self-contained unit to model pronunciation variation

Incremental syllable-context phonetic vocoding

HMM-Based Speech Segmentation: Improvements of Fully Automatic Approaches

Pronunciation variation generation for spontaneous speech synthesis using state-based voice transformation

Informative dialect recognition using context-dependent pronunciation modeling

A Deep Generative Architecture for Postfiltering in Statistical Parametric Speech Synthesis

Automatic Speech Recognition for Mixed Dialect Utterances by Mixing Dialect Language Models

Synthesis of Spontaneous Speech With Syllable Contraction Using State-Based Context-Dependent Voice Transformation