Reducing language context confusion for end-to-end code-switching automatic speech recognition. (arXiv:2201.12155v3 [cs.CL] UPDATED)

Code-switching deals with alternative languages in communication process.
Training end-to-end (E2E) automatic speech recognition (ASR) systems for
code-switching is especially challenging as code-switching training data are
always insufficient to combat the increased multilingual context confusion due
to the presence of more than one language. We propose a language-related
attention mechanism to reduce multilingual context confusion for the E2E
code-switching ASR model based on the Equivalence Constraint (EC) Theory. The
linguistics theory requires that any monolingual fragment that occurs in the …

arxiv automatic speech recognition code context language speech speech recognition