Automatic Segmentation of Parasitic Sounds in Speech Corpora for TTS Synthesis

Kód výsledku v 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>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Automatic Segmentation of Parasitic Sounds in Speech Corpora for TTS Synthesis

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Automatic Segmentation of Parasitic Sounds in Speech Corpora for TTS Synthesis

Popis výsledku anglicky

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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

—

Projekt

<a href="/cs/project/GA102%2F09%2F0989" target="_blank" >GA102/09/0989: Nové perspektivní metody vysoce kvalitní syntézy mluvené češtiny</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2010

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Lecture Notes in Artificial Intelligence

ISSN

0302-9743

e-ISSN

—

Svazek periodika

2010

Číslo periodika v rámci svazku

6231

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

8

Strana od-do

—

Kód UT WoS článku

—

EID výsledku v databázi Scopus

—