Dialectal Speech Recognition and Translation of Swiss German Speech to Standard German Text: Microsoft's Submission to SwissText 2021
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 8 of its 23 references that are in the map. First posted on arXiv 15 Jun 2021.
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8 of 23 references.
References · 8
- 2016Enriching Word Vectors with Subword Informationcited by 1,578
- 2014Deep Speech: Scaling up end-to-end speech recognitioncited by 354
- 2019Jasper: An End-to-End Convolutional Neural Acoustic Modelcited by 75
- 2016Achieving Human Parity in Conversational Speech Recognitioncited by 63
- 2020Swiss Parliaments Corpus, an Automatically Aligned Swiss German Speech to Standard German Text Corpuscited by 16
- 2021Arabic Speech Recognition by End-to-End, Modular Systems and Humancited by 12
- 2020A Swiss German Dictionary: Variation in Speech and Writingcited by 5
- 2017Acoustic data-driven lexicon learning based on a greedy pronunciation selection frameworkcited by 3
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3 papers.