This paper describes the winning approach in the Shared Task 3 at SwissText 2021 on Swiss German Speech to Standard German Text, a public competition on dialect recognition and translation. Swiss German refers to the multitude of Alemannic dialects spoken in the German-speaking parts of Switzerland. Swiss German differs significantly from standard German in pronunciation, word inventory and grammar. It is mostly incomprehensible to native German speakers. Moreover, it lacks a standardized written script. To solve the challenging task, we propose a hybrid automatic speech recognition system with a lexicon that incorporates translations, a 1st pass language model that deals with Swiss German particularities, a transfer-learned acoustic model and a strong neural language model for 2nd pass rescoring. Our submission reaches 46.04% BLEU on a blind conversational test set and outperforms the second best competitor by a 12% relative margin.
翻译:本文描述了瑞士德文对标准德文的德国语演讲2021瑞士文本第2021号瑞士文本第3号共同任务中获胜的方法。瑞士德文提到在瑞士德语地区讲的多种阿莱曼尼方言。瑞士德文在发音、文字目录和语法方面与德文大相径庭。德语母语人基本上无法理解。此外,它缺乏标准化的书面文字。为了解决这项具有挑战性的任务,我们提议建立一个混合自动语音识别系统,配有包含翻译的词汇系统,一个处理瑞士德文特殊性的第一流语言模型,一个传输-感知声学模型和一个强大的神经语言模型,用于第二通关。我们的呈件在盲话测试中达到了46.04%的BLEU,并且比第二最佳竞争者高出12%的相对差幅。