Study Finds Multi-task Learning Degrades Surface-level Transcription in Second Language Speech Recognition
A new paper accepted at the ICML 2026 Workshop on Machine Learning for Audio finds that multi-task learning (MTL) degrades surface-level transcription accuracy in second-language (L2) speech recognition, even as it improves meaning-level output. The degradation is most pronounced in English and correlates with the divergence between surface pronunciation and intended meaning, as measured by Levenshtein edit distance. The findings challenge a core assumption of MTL—that shared representations benefit all tasks equally—and call for new framework designs that reduce encoder-level entanglement.
Researchers studying second-language (L2) automatic speech recognition (ASR) have identified a fundamental limitation of multi-task learning (MTL) when the system must simultaneously produce both surface transcriptions (what was actually said) and meaning transcriptions (what was intended). Testing across Korean and English, the study found that while MTL improved meaning-level output, it consistently degraded surface transcription quality, with English showing worse degradation than Korean. The severity of surface degradation scaled with the phonetic-semantic divergence between the two outputs, quantified using Levenshtein edit distance. Encoder-level analysis revealed that Korean maintained more distinct internal representations for each task, whereas English produced nearly identical encoder representations—a phenomenon the authors term 'representational entanglement.' Decoder-level analysis further showed that the meaning-focused decoder adapted by developing a unique representation, while the surface-focused decoder remained constrained by the entangled encoder output. The authors argue these findings motivate the development of MTL architectures specifically designed to disentangle task representations at the encoder level. The paper is five pages long, includes two figures, and has been accepted to the 43rd International Conference on Machine Learning Workshop on Machine Learning for Audio.
What's missing
The study tests only two languages (Korean and English), leaving open whether the observed entanglement patterns generalize to other language pairs or L2 combinations with different phonological properties. The paper does not propose or evaluate a concrete architectural solution to the entanglement problem, only motivating future work.
What different sources said
- arXiv cs.CLCenter
Multi-task Learning is Not Enough: Representational Entanglement in Dual-output Second Language Speech Recognition
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