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 [BibTeX] [Marc21]
An Evaluation Benchmark for Automatic Speech Recognition of German-English Code-Switching
Type of publication: Conference paper
Citation: Khosravani_ASRU-2_2021
Booktitle: IEEE Automatic Speech Recognition and Understanding Workshop
Year: 2021
Month: December
Abstract: Code-switching arises when a (typically multilingual) speaker changes language during an utterance. This linguistic phenomenon causes problems for automatic speech recognition as the models are typically monolingual. In this work, we present a code-switching evaluation scenario for German-English that is created by resegmenting the German Spoken Wikipedia Corpus. Since these articles span a wide variety of (often technical) topics, they include a lot of borrowing and code-switching phenomena. The resulting corpus consists of around 34 hours of intra-sentential switches. We investigate end-to-end approaches using both monolingual and multilingual automatic speech recognition as well as language modeling to address the code-switching scenario. Results suggest that multilingual sequence-to-sequence approaches are to be preferred for code-switching thanks to the power of the attention mechanism. The segments are made available to the community as a benchmark.
Keywords: Automatic Speech Recognition, benchmarks, Code-Switching, German, Multilingual
Projects Idiap
Authors Khosravani, Abbas
Garner, Philip N.
Lazaridis, Alexandros
Added by: [UNK]
Total mark: 0
  • Khosravani_ASRU-2_2021.pdf