Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Algorithms for Automatic Accentuation and Transcription of Russian Texts in Speech Recognition Systems. / Yakovenko, Olga; Bondarenko, Ivan; Borovikova, Mariya и др.
Speech and Computer - 20th International Conference, SPECOM 2018, Proceedings. ред. / A Karpov; O Jokisch; R Potapova. Springer-Verlag GmbH and Co. KG, 2018. стр. 768-777 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Том 11096 LNAI).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
}
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 -
ID: 16703931