System for producing subtitles to internet audio-visual documents

Nouza, Jan; Blavka, Karel; Bohac, Marek; Červa, Petr; Málek, Jiřı́

doi:10.1109/tsp.2015.7296415

Cited by 4 publications

(1 citation statement)

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“…It has been designed on a modular base whose components have been already utilized to build such practical applications, like, dictation programs [1], a tool for on-line or off-line video sub-titling [2], a system for continual monitoring of TV and radio stations [3], and recently also a platform for automatic transcription and search in a huge historical audio archive of Czech Radio [4]. The modules of the developed system have been designed in the way that allows them to be adapted to another language, in particular, to other Slavic languages that share some specific features similar to Czech.…”

Section: Introductionmentioning

confidence: 99%

Methods for rapid development of automatic speech recognition system for Russian

Safarik

Nouza

2015

2015 IEEE International Workshop of Electronics, Control, Measurement, Signals and Their Application to Mechatronics (ECMSM)

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In this paper we present our approach to the rapid and efficient development of an automatic speech recognition (ASR) system for Russian. We try to utilize our tools, procedures and data previously designed and collected for other Slavic languages, Czech and Slovak. We show how we build a large corpus of texts acquired from major publishers' web pages and convert it from Cyrillic to Latin to simplify further processing. The corpus is used to create a representative lexicon with 218K words and 259K pronunciations and a probabilistic language model. When training the acoustic model (AM), we use the GlobalPhone database of recordings and a largely automated scheme that includes bootstrapping with an existing Czech AM and several iterative steps to gradually improve both phonetic annotations and the target Russian AM. The recent prototype of the Russian ASR system is evaluated on the test part of the GlobalPhone database and achieves 18.2 % word error rate.

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Section: Introductionmentioning

confidence: 99%