Application of russian language phonemics to generate macedonian speech recognition model using sphinx
Date Issued
2016-09
Author(s)
Mingov, Riste
Abstract
Speech-recognition provides possibilities for improved user
experience and new level of features in various applications. Although
there are widely available open-source and proprietary systems for speech
recognition and synthesis for the more widely used languages, there are
no available and robust enough systems for the Macedonian language.
Building a speech recognition system requires a lot of high-quality recordings, which is expensive operation. To overcome this issue we propose
applying some background knowledge and using existing speech synthesis engines to train speech recognition system. Since the Macedonian
language belongs to the group of Slavic languages, there are many similarities between them. In this paper we apply this fact to generate a
speech synthesis module for the Macedonian language based on the Russian language model. Furthermore, we use the speech synthesis module
to build a speech recognition module using the CMUSphinx Toolkit.
Finally the results are presented and they confirm that a system with
substantial quality can be built without the need of manual recordings
in the specific language.
experience and new level of features in various applications. Although
there are widely available open-source and proprietary systems for speech
recognition and synthesis for the more widely used languages, there are
no available and robust enough systems for the Macedonian language.
Building a speech recognition system requires a lot of high-quality recordings, which is expensive operation. To overcome this issue we propose
applying some background knowledge and using existing speech synthesis engines to train speech recognition system. Since the Macedonian
language belongs to the group of Slavic languages, there are many similarities between them. In this paper we apply this fact to generate a
speech synthesis module for the Macedonian language based on the Russian language model. Furthermore, we use the speech synthesis module
to build a speech recognition module using the CMUSphinx Toolkit.
Finally the results are presented and they confirm that a system with
substantial quality can be built without the need of manual recordings
in the specific language.
Subjects
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