Estimation of Articulatory Features for Czech Language

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F14%3A00220036" target="_blank" >RIV/68407700:21230/14:00220036 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Estimation of Articulatory Features for Czech Language

Popis výsledku v původním jazyce

The issues of automatic speech recognition (ASR) aimed at the Czech language have been intensively studied in the past decades. The researches have successfully managed to develop several practical applications such as dictation programs, automatic broadcast transcription (subtitling) and others. Accuracy of these ASR systems is generally satisfactory high, however it is significantly lower if the signal is corrupted, e.g. in the case of high-level background noise, spontaneous speech or when speech ismasked and pronounced in a reduced form. These issues are still an obstacle for a wider usage of voice recognition technology under such conditions, because commonly achieved WER (Word Error Rate) of spontaneous speech recognition is above 50% in average. A possible solution to overcome this deficiency can be in the usage of speech production knowledge within ASR systems. Consequently, the speech production knowledge based on articulatory features (AFs) starts being used more often at fe

Název v anglickém jazyce

Estimation of Articulatory Features for Czech Language

Popis výsledku anglicky

The issues of automatic speech recognition (ASR) aimed at the Czech language have been intensively studied in the past decades. The researches have successfully managed to develop several practical applications such as dictation programs, automatic broadcast transcription (subtitling) and others. Accuracy of these ASR systems is generally satisfactory high, however it is significantly lower if the signal is corrupted, e.g. in the case of high-level background noise, spontaneous speech or when speech ismasked and pronounced in a reduced form. These issues are still an obstacle for a wider usage of voice recognition technology under such conditions, because commonly achieved WER (Word Error Rate) of spontaneous speech recognition is above 50% in average. A possible solution to overcome this deficiency can be in the usage of speech production knowledge within ASR systems. Consequently, the speech production knowledge based on articulatory features (AFs) starts being used more often at fe

Druh

O - Ostatní výsledky

CEP obor

JA - Elektronika a optoelektronika, elektrotechnika

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

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