This paper presents an overview of rule-based system for automatic accentuation and phonemic transcription of Russian texts for speech connected tasks, such as Automatic Speech Recognition (ASR). Two parts of the developed system, accentuation and transcription, use different approaches to achieve correct phonemic representations of input phrases. Accentuation is based on “Grammatical dictionary of the Russian language” of A.A. Zaliznyak and wiktionary corpus. To distinguish homographs, the accentuation system also utilises morphological information of the sentences based on Recurrent Neural Networks (RNN). Transcription algorithms apply the rules presented in the monograph of B.M. Lobanov and L.I. Tsirulnik “Computer Synthesis and Voice Cloning”. The rules described in the present paper are implemented in an open-source module, which can be of use to any scientific study connected to ASR or Speech To Text (STT) tasks. Resulting system has shown 98.3% phone accuracy on a test set of 63 sentences (and 200 phonetic syntagms) which were marked up manually by linguists. The developed toolkit is written in the Python language and is accessible on Github for any researcher interested.
|Number of pages||13|
|Journal||Komp'juternaja Lingvistika i Intellektual'nye Tehnologii|
|Publication status||Published - 1 Jan 2018|
|Event||2018 International Conference on Computational Linguistics and Intellectual Technologies, Dialogue 2018 - Moscow, Russian Federation|
Duration: 30 May 2018 → 2 Jun 2018
- Automatic speech recognition
- Rule-based phonemic transcription