NDLI: Symbolic modeling of prosody: from linguistics to statistics

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IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP) : Volume 24

IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP) : Volume 23

Issue 12, December 2015

Issue 11, November 2015

Issue 10, Octember 2015

Issue 9, September 2015

Introduction to the special section on continuous space and related methods in natural language processing

Syntactic and semantic features for code-switching factored language models

Graph-based lexicon regularization for PCFG with latent annotations

Distributed feature representations for dependency parsing

Learning semantic hierarchies: a continuous vector space approach

Adequacy-fluency metrics: evaluating MT in the continuous space model framework

Topic-based coherence modeling for statistical machine translation

A sparse plus low-rank exponential language model for limited resource scenarios

Deep learning framework with confused sub-set resolution architecture for automatic Arabic diacritization

From feedforward to recurrent LSTM neural networks for language modeling

Using recurrent neural networks for slot filling in spoken language understanding

Robust sound event classification using deep neural networks

Text search of surnames in some slavic and other morphologically rich languages using rule based phonetic algorithms

Supervised detection and unsupervised discovery of pronunciation error patterns for computer-assisted language learning

Voice conversion using RNN pre-trained by recurrent temporal restricted boltzmann machines

Symbolic modeling of prosody: from linguistics to statistics

Issue 2, February 2015

Issue 1, January 2015

IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP) : Volume 22

Symbolic modeling of prosody: from linguistics to statistics

Content Provider	ACM Digital Library
Author	Obin, Nicolas Lanchantin, Pierre
Copyright Year	2016
Abstract	The assignment of prosodic events (accent and phrasing) from the text is crucial in text-to-speech synthesis systems. This paper addresses the combination of linguistic and metric constraints for the assignment of prosodic events in text-to-speech synthesis. First, a linguistic processing chain is used to provide a rich linguistic description of a text. Then, a novel statistical representation based on a hierarchical HMM (HHMM) is used to model the prosodic structure of a text: the root layer represents the text, each intermediate layer a sequence of intermediate phrases, the pre-terminal layer the sequence of accents, and the terminal layer the sequence of linguistic contexts. For each intermediate layer, a segmental HMM and information fusion are used to fuse the linguistic and metric constraints for the segmentation of a text into phrases. A set of experiments conducted on multi-speaker databases with various speaking styles reports that: the rich linguistic representation improves drastically the assignment of prosodic events, and the fusion of linguistic and metric constraints significantly improves over standard methods for the segmentation of a text into phrases. These constitute substantial advances that can be further used to model the speech prosody of a speaker, a speaking style, and emotions for text-to-speech synthesis.
Starting Page	588
Ending Page	599
Page Count	12
File Format	PDF
ISSN	23299290
e-ISSN	23299304
DOI	10.1109/TASLP.2014.2387389
Volume Number	23
Issue Number	3
Journal	IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-03-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Dempster-Shafer fusion Hierarchical HMMs Prosodic events Segmental HMMs Speaking style Speech prosody Surface/deep syntactic parsing Text-to-speech synthesis
Content Type	Text
Resource Type	Article
Subject	Instrumentation Computational Mathematics Signal Processing Electrical and Electronic Engineering Acoustics and Ultrasonics Speech and Hearing Media Technology

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Reformulating Prosodic Break Model into Segmental HMMs and (2011)

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MeLos: Analysis and Modelling of Speech Prosody and Speaking Style

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Symbolic modeling of prosody: from linguistics to statistics

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