Automatic Segmentation of Parasitic Sounds in Speech Corpora for TTS Synthesis
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F10%3A00504448" target="_blank" >RIV/49777513:23520/10:00504448 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Automatic Segmentation of Parasitic Sounds in Speech Corpora for TTS Synthesis
Original language description
In this paper, automatic segmentation of parasitic speech sounds in speech corpora for text-to-speech (TTS) synthesis is presented. The automatic segmentation is, beside the automatic detection of the presence of such sounds in speech corpora, an important step in the precise localisation of parasitic sounds in speech corpora. The main goal of this study is to find out whether the segmentation of these sounds is accurate enough to enable cutting the sounds out of synthetic speech or explicit modelling of these sounds during synthesis. HMM-based classifier was employed to detect the parasitic sounds and to find the boundaries between these sounds and the surrounding phones simultaneously. The results show that the automatic segmentation of parasitic sounds is comparable to the segmentation of other phones, which indicates that the cutting out or the explicit usage of parasitic sounds should be possible.
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GA102%2F09%2F0989" target="_blank" >GA102/09/0989: New innovative methods for high-quality synthesis of Czech speech</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Lecture Notes in Artificial Intelligence
ISSN
0302-9743
e-ISSN
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Volume of the periodical
2010
Issue of the periodical within the volume
6231
Country of publishing house
DE - GERMANY
Number of pages
8
Pages from-to
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UT code for WoS article
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EID of the result in the Scopus database
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