为俄语语音识别开发了自动重音标注与音素转写工具
Algorithms For Automatic Accentuation And Transcription Of Russian Texts In Speech Recognition Systems
- 基于语法词典和神经网络区分同音词进行重音标注
- 使用Лобанов的规则实现音素转写，训练模型达71.2%词准确率
- 开源工具可直接用于俄语语音识别研究，支持学术复现
本文介绍了一种用于俄语语音识别等连贯语音任务的规则型自动重音标注与音素转写系统。该系统分为重音标注和转写两部分，分别采用不同方法实现输入语句的正确音素表示。重音标注基于А.А. Зализняка的《俄语语法词典》和维基词典语料库，通过循环神经网络（RNN）利用句子的形态学信息区分同音词。转写算法遵循Л.И. Цирульник и Б.М. Лобанов的《计算机合成与语音克隆》中的规则。所提规则已实现为开源模块，可用于任何与语音识别（ASR）或语音转文本（STT）相关的科研工作。使用俄语Voxforge数据库的自动标注文本作为CMU Sphinx声学模型的训练数据，经交叉验证评估，平均词准确率为71.2%。该工具包用Python编写，已在GitHub公开，供研究人员使用。
原文摘要 · Abstract (English)
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.
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