Multilingual TASU: Extending Text-Only Alignment to Multilingual Speech Recognition via Simulated CTC Posteriors

Abstract

Speech Large Language Models (Speech LLMs) achieve strong multilingual ASR when large-scale paired speech-text data is available, yet such resources remain scarce and highly imbalanced across languages. Text-Only Alignment for Speech Understanding (TASU) alleviates this bottleneck by training cross-modal alignment from simulated CTC posteriors without paired audio, but prior work has been limited to English, leaving open whether text-driven alignment can scale to the multilingual setting where data sparsity and cross-lingual phonetic interference amplify the difficulty of learning a shared alignment interface. In this work, we investigate TASU-style posterior alignment as a means of mitigating paired-data scarcity in multilingual ASR. We adopt the native 50,002-way posterior space of a frozen OWSM-CTC frontend as a shared multilingual interface to Qwen2.5-1.5B-Instruct, and systematically compare three supervision sources on eight Multilingual LibriSpeech languages: real audio-derived posteriors, heuristic CTC Posterior Simulation (CPS), and posteriors generated by a learned multilingual simulator. With real posteriors, LoRA adaptation reduces pooled WER from 10.08% (standalone OWSM-CTC) to 8.21%, improving all eight languages. For text-only alignment, the learned simulator substantially outperforms heuristic CPS and attains 10.51% WER with LoRA. Notably, a CPS-initialized model reaches 9.19% WER after only one real-posterior adaptation epoch using merely one-ninth of the complete MLS paired-speech training set, demonstrating that text-first initialization can drastically reduce the amount of paired audio required for competitive multilingual ASR. These findings establish TASU as a viable multilingual alignment framework and highlight text-only simulation as an effective remedy for paired-data scarcity and imbalance across languages.