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Dialectal Speech Recognition and Translation of Swiss German Speech to Standard German Text: Microsoft's Submission to SwissText 2021

arXiv 2106.08126 ↗First posted 15 Jun 2021eess.AS cs.CL cs.LGcited by 3

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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

  1. 2016Enriching Word Vectors with Subword Informationcited by 1,578
  2. 2014Deep Speech: Scaling up end-to-end speech recognitioncited by 354
  3. 2019Jasper: An End-to-End Convolutional Neural Acoustic Modelcited by 75
  4. 2016Achieving Human Parity in Conversational Speech Recognitioncited by 63
  5. 2020Swiss Parliaments Corpus, an Automatically Aligned Swiss German Speech to Standard German Text Corpuscited by 16
  6. 2021Arabic Speech Recognition by End-to-End, Modular Systems and Humancited by 12
  7. 2020A Swiss German Dictionary: Variation in Speech and Writingcited by 5
  8. 2017Acoustic data-driven lexicon learning based on a greedy pronunciation selection frameworkcited by 3

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