AN EVALUATION BENCHMARK FOR AUTOMATIC SPEECH RECOGNITION OF GERMAN-ENGLISH CODE-SWITCHING

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 multi-lingual 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.