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.
Abstract (translated)
本文概述了一个基于规则的自动重音和音素转录的俄罗斯文本系统,用于语音连接任务,如自动语音识别(ASR)。系统的两个部分,重音和转录,采用不同的方法来实现输入短语的正确音素表示。重音基于A.A. Zaliznyak和维基词典俄语的“语法词典”。为了区分同形词,重音系统还利用基于循环神经网络(RNN)的句子的语素信息。转录算法应用了B.M. Lobanov和L.I. Tsirulnik在其著作中提出的规则。本文中的规则描述在文中得到了实现,这是一个开源模块,对于与ASR或语音到文本(STT)任务相关的任何科学研究都很有用。在CMU Sphinx上使用俄语Voxforge数据库的自动标注的文本注释作为音频模型的训练数据。通过交叉验证评估该音频模型,平均单词准确率达到了71.2%。开发工具包是用Python编写的,并可公开获取在GitHub上。
URL
https://arxiv.org/abs/2410.02538
