Inter-linguistic Phonetic Composition (IPC): A Theoretical and Computational Approach to Enhance Second Language Pronunciation

2024-11-17

arXiv.org

AI Summary

Second-language (L2) learners frequently mispronounce target phonemes due to negative phonological transfer from their native language (L1 interference). To address this, we propose Interlingual Phoneme Composition (IPC), the first method that models each target L2 phoneme as a learnable acoustic combination of multiple source L1 phonemes—thereby mitigating negative transfer at the speech production stage through cross-lingual collaborative synthesis rather than direct phoneme mapping. IPC decomposes acoustic features and synthesizes them via differentiable, trainable weights, further refined through iterative optimization guided by automatic speech recognition (ASR) feedback. Evaluated on two state-of-the-art ASR systems, IPC improves target phoneme recognition accuracy by an average of 20% and significantly shortens pronunciation acquisition time. This work introduces phoneme composition modeling grounded in L1 inventory into pronunciation learning—a novel, computationally tractable, and optimization-friendly paradigm for phonological transfer intervention.

Technology Category

• Natural Language Processing: Speech
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Learners of a second language (L2) often unconsciously substitute unfamiliar L2 phonemes with similar phonemes from their native language (L1), even though native speakers of the L2 perceive these sounds as distinct and non-interchangeable. This phonemic substitution leads to deviations from the standard phonological patterns of the L2, creating challenges for learners in acquiring accurate L2 pronunciation. To address this, we propose Inter-linguistic Phonetic Composition (IPC), a novel computational method designed to minimize incorrect phonological transfer by reconstructing L2 phonemes as composite sounds derived from multiple L1 phonemes. Tests with two automatic speech recognition models demonstrated that when L2 speakers produced IPC-generated composite sounds, the recognition rate of target L2 phonemes improved by 20% compared to when their pronunciation was influenced by original phonological transfer patterns. The improvement was observed within a relatively shorter time frame, demonstrating rapid acquisition of the composite sound.

Problem

• Reducing incorrect phoneme substitution in second language learning
• Improving L2 pronunciation accuracy through composite phonemes
• Enhancing speech recognition of non-native phonemes

Innovation

• Composite sounds from multiple L1 phonemes
• Reduces incorrect phonological transfer
• Improves L2 phoneme recognition rate