Multi-task Learning is Not Enough: Representational Entanglement in Dual-output Second Language Speech Recognition

2026-06-08 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Expert, long

Summary

A study on dual-output second-language (L2) automatic speech recognition (ASR) investigates multi-task learning (MTL) for simultaneously transcribing pronunciations (surface-level) and intended meanings (meaning-oriented). The research, using Korean (41,803 samples) and English (72,022 samples) L2 datasets, reveals that MTL improves meaning transcription but degrades surface transcription, with degradation significantly larger in English. This asymmetry scales with surface-meaning divergence, measured by Levenshtein edit distance (ED), particularly in English where the surface CER gap increases monotonically from +0.28 at ED=0 to +6.72 at ED>10. Encoder analysis using Centered Kernel Alignment (CKA) shows Korean preserves distinct task representations, while English exhibits encoder-level entanglement with nearly identical representations. Cross-task decoder analysis indicates the English meaning decoder adapts with a unique representation, effectively bypassing the entangled encoder, whereas the surface decoder remains constrained. Models were trained with AdamW, learning rates 10^-4 or 10^-5, for 50 epochs on an NVIDIA RTX 3090 GPU.

Key takeaway

For Machine Learning Engineers developing dual-output L2 ASR systems, you should be aware that multi-task learning can degrade surface transcription, especially for languages with high surface-meaning divergence like English. Your current MTL approach might suffer from encoder-level representational entanglement, limiting performance. Consider implementing structured MTL frameworks that actively mitigate this entanglement, such as sparse decomposition or adversarial training, to improve surface transcription accuracy without sacrificing meaning-oriented performance.

Key insights

Multi-task learning for L2 ASR can degrade surface transcription due to encoder-level representational entanglement.

Principles

• Encoder-level entanglement causes asymmetric MTL performance.
• Decoder adaptation can partially bypass entangled encoder representations.
• Surface-meaning divergence correlates with MTL degradation in some languages.

Method

Compare single-output (SO) models (separate encoder/decoder per task) with dual-output (DO) models (shared encoder, two decoders) using CTC-attention. Analyze representations via CKA.

In practice

• Design MTL frameworks to mitigate encoder-level entanglement.
• Consider sparse decomposition or adversarial training for L2 ASR.
• Evaluate cross-task decoder alignment for representational flexibility.

Topics

• Multi-task Learning
• Second Language ASR
• Representational Entanglement
• Encoder-Decoder Architectures
• Cross-lingual Analysis
• Speech Transcription

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.