Algorithms For Automatic Accentuation And Transcription Of Russian Texts In Speech Recognition Systems

TL;DR

This paper introduces a rule-based system for automatic accentuation and phonemic transcription of Russian texts, enhancing speech recognition accuracy with open-source tools and neural network integration.

Contribution

It presents a novel combination of rule-based and neural network approaches for Russian text accentuation and transcription, implemented in an accessible open-source Python toolkit.

Findings

Achieved 71.2% mean Word Accuracy on Russian Voxforge database

Developed an open-source Python module for accentuation and transcription

Utilized neural networks for homograph disambiguation

Abstract

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.