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 | |
| Added by: | [UNK] |
| Total mark: | 0 |
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