NDLI: Intonational phrase break prediction for text-to-speech synthesis using dependency relations

Content Provider	IEEE Xplore Digital Library
Author	Mishra, T. Yeon-jun Kim Bangalore, S.
Copyright Year	2015
Description	Author affiliation: Interactions, Franklin, MA, USA (Mishra, T.; Yeon-jun Kim; Bangalore, S.)
Abstract	Intonational phrase (IP) break prediction is an important aspect of front-end analysis in a text-to-speech system. Standard approaches for intonational phrase break prediction rely on the use of linguistic rules or more recently, lexicalized data-driven models. Linguistic rules are not robust while data-driven models based on lexical identity do not generalize across domains. To overcome these challenges, in this paper, we explore the use of syntactic features to predict intonational phrase breaks. On a test set of over 40 thousand words, while a lexically driven IP break prediction model yields an F-score of 0.82, a non-lexicalized model that uses part-of-speech tags and dependency relations achieves an F-score of 0.81 with added feature of being more portable across domains. In this work, we also examine the effect of contextual information on prediction performance. Our evaluation shows that using a three-token left context in a POS-tag based model results in only a 2% drop in recall compared to a model that uses both a left and right context, which suggests the viability of using such a model for incremental text-to-speech system.
Starting Page	4919
Ending Page	4923
File Size	209476
Page Count	5
File Format	PDF
ISBN	9781467369978
DOI	10.1109/ICASSP.2015.7178906
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-19
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Context Computational modeling IP networks Context modeling Speech Syntactics text-analysis Intonational phrase phrase breaks IP prediction prosody
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

Apply length distribution model to intonational phrase prediction

Experimental evaluation of tree-based algorithms for intonational breaks representation (2005).

Investigating effect of rich syntactic features on Mandarin prosodic phrase boundaries prediction

Prosody phrase break prediction in Vietnamese using decision tree

Syntax based prosody modeling using HMM for Bahasa Indonesia

Assamese Intonation Modeling for Speech Synthesis

Prosody modeling for mandarin exclamatory speech

Data-driven phrasing for speech synthesis in low-resource languages

Speech prosody control using weighted neural network ensembles

Intonational phrase break prediction for text-to-speech synthesis using dependency relations

Similar Documents

Apply length distribution model to intonational phrase prediction

Experimental evaluation of tree-based algorithms for intonational breaks representation (2005).

Investigating effect of rich syntactic features on Mandarin prosodic phrase boundaries prediction

Prosody phrase break prediction in Vietnamese using decision tree

Syntax based prosody modeling using HMM for Bahasa Indonesia

Assamese Intonation Modeling for Speech Synthesis

Prosody modeling for mandarin exclamatory speech

Data-driven phrasing for speech synthesis in low-resource languages

Speech prosody control using weighted neural network ensembles

Intonational phrase break prediction for text-to-speech synthesis using dependency relations