NDLI: Personalized natural speech synthesis based on retrieval of pitch patterns using hierarchical Fujisaki model

Content Provider	IEEE Xplore Digital Library
Author	Yi-Chin Huang Chung-Hsien Wu Shih-Lun Lin
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. Sci. & Inf. Eng., Nat. Cheng-Kung Univ., Tainan, Taiwan (Yi-Chin Huang; Chung-Hsien Wu; Shih-Lun Lin)
Abstract	In recent years, speech synthesis based on Hidden Markov Model (HMM) has been developed, which can synthesize stable and intelligible speech with flexibility and small footprint. However, synthesized prosodic features are still incapable to convey personalization and natural property. Previous prosody models, mainly constructed from the clustered prosodic features, are unable to characterize personalized prosodic information as the linguistic cues of the input sentence are indistinguishable for all speakers. An approach to retrieval of personalized pitch patterns from the real speech corpus of the target speaker, is proposed, incorporating with the HMM-based speech synthesizer, to generate a personalized natural pitch contour. The modified Fujisaki model is adopted to depict the hierarchical pitch patterns, aiming to model local pitch contour variation and global intonation of utterances in the corpus. The codeword sequences of utterances in the training and the synthesized corpora are constructed and used to obtain the relationship of pitch patterns between the real and synthesized speech. Finally, a language model of pitch pattern is constructed to obtain an optimal pitch pattern sequence of the input sentence. The experimental results using subjective and objective evaluations demonstrated the proposed approach can substantially outperform the conventional statistical synthesis methods, in terms of naturalness and speaker similarity.
Starting Page	7844
Ending Page	7848
File Size	1152934
Page Count	5
File Format	PDF
ISBN	9781479903566
ISSN	15206149
DOI	10.1109/ICASSP.2013.6639191
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-26
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Speech Hidden Markov models Speech synthesis Training Acoustics Splines (mathematics) Personalized Speech Synthesis Fujisaki Model Hierarchical Prosodic Structure Pattern Retrieval
Content Type	Text
Resource Type	Article

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

Hierarchical prosodic pattern selection based on Fujisaki model for natural mandarin speech synthesis

Pitch adaptive training for hmm-based singing voice synthesis

Modeling pitch trajectory by hierarchical HMM with minimum generation error training

Automatic phrase boundary labeling for Mandarin TTS corpus using context-dependent HMM

Unsupervised prosodic phrase boundary labeling of Mandarin speech synthesis database using context-dependent HMM

Integration of speaker and pitch adaptive training for HMM-based singing voice synthesis

Prosody modification and fujisaki’s model: preserving natural soundness.

Hierarchical pitch target model for Mandarin speech

Multi-speaker prosodic instance selection for HMM-based speech synthesis

Personalized natural speech synthesis based on retrieval of pitch patterns using hierarchical Fujisaki model

Similar Documents

Hierarchical prosodic pattern selection based on Fujisaki model for natural mandarin speech synthesis

Pitch adaptive training for hmm-based singing voice synthesis

Modeling pitch trajectory by hierarchical HMM with minimum generation error training

Automatic phrase boundary labeling for Mandarin TTS corpus using context-dependent HMM

Unsupervised prosodic phrase boundary labeling of Mandarin speech synthesis database using context-dependent HMM

Integration of speaker and pitch adaptive training for HMM-based singing voice synthesis

Prosody modification and fujisaki’s model: preserving natural soundness.

Hierarchical pitch target model for Mandarin speech

Multi-speaker prosodic instance selection for HMM-based speech synthesis

Personalized natural speech synthesis based on retrieval of pitch patterns using hierarchical Fujisaki model