Paper 5: Compositional Generalization and Speaker Adaptation in Script-Aware ASR

# Paper 5: Compositional Generalization and Speaker Adaptation in Script-Aware ASR ## Thesis N'Ko's phonetic transparency advantage (Paper 4) extends beyond controlled CER comparison: N'Ko generalizes better to unseen vocabulary (Exp F), enables zero-shot vocabulary expansion via graph update (Exp H), and adapts faster to new speakers through test-time training (Exp G). Together, these three experiments demonstrate that phonetically transparent scripts produce ASR systems with superior operational lifetime characteristics. ## Core Argument Paper 4 showed that trajectory-biased CTC gives N'Ko -5.25pp CER advantage over Latin at 297K scale. Paper 5 asks: does this advantage persist in deployment scenarios where the model encounters words and speakers it never saw in training? ## Section 1: Introduction - ASR systems degrade in deployment: new vocabulary, new speakers, domain shift - For under-resourced scripts like N'Ko, these problems are amplified (no large-scale fine-tuning data available) - We test three deployment scenarios: compositional generalization, vocabulary expansion, speaker adaptation - All experiments use the same 297K-pair controlled setup from Paper 4 ## Section 2: Related Work - Compositional generalization in ASR (cite subword approaches, BPE vs character-level) - Zero-shot vocabulary expansion (cite KG-augmented ASR, semantic priming) - Test-time training / adaptation (cite TTT, few-shot speaker adaptation, meta-learning for ASR) - Script-dependent effects in multilingual ASR (cite Whisper, MMS)