Multi-task Learning is Not Enough: Representational Entanglement in Dual-output Second Language Speech Recognition
arXiv:2606.06065v1 Announce Type: new Abstract: Second-language (L2) speech recognition often requires transcriptions of pronunciations and intended meanings. Multi-task learning (MTL) is a natural approach because it assumes that shared representations benefit both outputs. However, this paper shows that this assumption does not hold across Korean and English. MTL improves meaning but degrades surface transcription, especially in English, where the degradation scales with surface-meaning divergence measured by Levenshtein edit distance.Encoder analysis links these patterns to encoder-level en
This research is published as AI advancements push the boundaries of speech recognition, particularly for diverse linguistic data and multi-task applications.
It highlights a critical limitation in multi-task learning for second language speech recognition, suggesting existing architectural assumptions may hinder rather than help in specific linguistic contexts.
The understanding of representational entanglement in multi-task learning for L2 speech recognition, indicating that a 'one-size-fits-all' approach may be detrimental to performance in certain areas.
- · Researchers developing specialized AI architectures for L2 speech
- · Companies offering targeted linguistic AI solutions
- · Developers relying solely on generic multi-task learning for L2 speech
- · Platforms providing undifferentiated L2 speech recognition services
Further research will focus on disentangling representations in multi-task models for complex linguistic tasks.
This could lead to more robust and accurate second language AI speech recognition tailored to specific language pairs and their unique challenges.
Improved L2 speech recognition could enhance cross-cultural communication tools and language learning applications, but also raise new questions about data sovereignty and the digital divide in AI access.
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Read at arXiv cs.CL