TY - GEN

T1 - Algorithms for Automatic Accentuation and Transcription of Russian Texts in Speech Recognition Systems

AU - Yakovenko, Olga

AU - Bondarenko, Ivan

AU - Borovikova, Mariya

AU - Vodolazsky, Daniil

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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. Automatically marked up text annotations of the Russian Voxforge database were used as training data for an acoustic model in CMU Sphinx. The resulting acoustic model was evaluated on cross-validation, mean Word Accuracy being 71.2%. The developed toolkit is written in the Python language and is accessible on GitHub for any researcher interested.

AB - 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. Automatically marked up text annotations of the Russian Voxforge database were used as training data for an acoustic model in CMU Sphinx. The resulting acoustic model was evaluated on cross-validation, mean Word Accuracy being 71.2%. The developed toolkit is written in the Python language and is accessible on GitHub for any researcher interested.

KW - Accentuation

KW - Automatic speech recognition

KW - Corpora

KW - Rule-based phonemic transcription

UR - http://www.scopus.com/inward/record.url?scp=85053780912&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-99579-3_78

DO - 10.1007/978-3-319-99579-3_78

M3 - Conference contribution

AN - SCOPUS:85053780912

SN - 9783319995786

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 768

EP - 777

BT - Speech and Computer - 20th International Conference, SPECOM 2018, Proceedings

A2 - Karpov, A

A2 - Jokisch, O

A2 - Potapova, R

PB - Springer-Verlag GmbH and Co. KG

T2 - 20th International Conference on Speech and Computer, SPECOM 2018

Y2 - 18 September 2018 through 22 September 2018

ER -