arXiv Computation and Language
Oct 8

Phoneme-Guided Initialization for LLM-based Speech Recognition

The paper introduces phoneme‑guided initialization for large language model‑based speech recognition. By pre‑training an audio encoder on speech‑to‑phoneme conversion and an LLM on phoneme‑to‑grapheme conversion, then fine‑tuning jointly on ASR, the method leverages phoneme‑mediated processing within an end‑to‑end framework. Experiments on Japanese, Chinese, Tatar, and Urdu demonstrate that this approach matches or surpasses both cascaded S2P‑P2G baselines and end‑to‑end models without phoneme initialization.

By Ryo Magoshi, Shinsuke Sakai, Tatsuya Kawahara
arXiv Computation and Language
6d ago

PEDBench: Can Speech Models Detect Phonetic Errors?

PEDBench is a new benchmark for phonetic error detection (PED) that includes natural recordings of children’s speech errors, scripted error productions by actors, and synthetic error productions generated via phone‑conditioned text‑to‑speech. The benchmark provides high‑quality phonetic annotations verified by trained annotators. Evaluation of 13 state‑of‑the‑art speech models shows that they struggle to reliably detect phonetic errors, even in controlled conditions, and often misclassify correct phonemes as errors. Fine‑tuned speech‑language models perform better on word‑level PED, but a sim‑to‑real domain gap persists.

By Zhonghao Shi, Zackary Rackauckas, Jasmine Yip, Savannah Halpern, Simon Campos, Lauren Brace, Emma Yang, Tiantian Feng, Xuan Shi, Shrikanth Narayanan, Teresa Satterfield, Ying Xu
Hugging Face Trending Papers
Jun 2

Efficient ASR Training with Conversations that Never Happened

Conversational ASR for lower-resource languages and niche domains is limited by the scarcity of domain-matched multi-speaker training data. We propose an augmentation pipeline that generates scenario-level dialogues with participant metadata, maps speaker attributes to TTS voice profiles, and assembles synthesized utterances into speaker-aware simulated conversations.

arXiv Computation and Language
Sep 18

Phoneme-guided TTS augmentation for ASR: A unified pipeline and multilingual evaluation

The paper introduces a phoneme-guided text-to-speech (TTS) augmentation pipeline for automatic speech recognition (ASR) that links multilingual speech generation with candidate-text selection and reference-speech quality control. It proposes phoneme-frequency-guided selection (PFGS), which prioritizes candidate texts containing common phonetic content based on real ASR training transcripts. Experiments across four languages and 13 test sets show that random text selection improves recognition on 11 test sets, while PFGS further improves nine test sets with relative word error rate reductions up to 19.3%, and reference-speech filtering also contributes to performance gains.

By Zhen Wang, TianRui Wu, RongQi Han, Hao Wu, Wei Liang, Wei Xu