Phonetic forced alignment is a key technique in phonetic research, yet existing alignment systems lack specialized models for low-resource language varieties. We address this by training text-dependent and text-independent aligners for Chengdu Mandarin using a 17-hour corpus and a custom G2P dictionary.
arXiv:2607. 21332v1 Announce Type: cross Abstract: Phonetic forced alignment is a key technique in phonetic research, yet existing alignment systems lack specialized models for low-resource language varieties.
The paper introduces two new corpus‑level, reference‑free metrics—Phoneme‑Cluster Mutual Information (PCMI) and Word Acoustic Consistency Score (WACS)—that use self‑supervised speech representations to evaluate forced alignment quality. PCMI quantifies how well aligned phoneme labels agree with clusters derived from SSL representations, while WACS assesses consistency across repeated word realizations via dynamic time warping of word representation sequences. Experiments on 85 languages from FLEURS and 45 languages in DoReCo show that both metrics degrade predictably under alignment perturbations, effectively distinguish high‑ from low‑quality alignments, and correlate strongly with traditional timestamp‑based measures, enabling scalable, multilingual alignment evaluation without manual annotations.
arXiv:2609. 21362v1 Announce Type: new Abstract: Conventional tokenizers represent text as characters or statistically derived subwords, overlooking the internal phonological structure of syllables and often requiring large vocabularies.
The paper introduces a unified phoneme‑based TTS‑to‑ASR augmentation pipeline that uses a multilingual TTS model with language‑ID conditioning and incorporates grapheme‑to‑phoneme conversion, reference‑speech filtering, and candidate‑text selection. It proposes phoneme‑frequency‑guided selection (PFGS) to rank sentences based on phoneme frequencies from real ASR labels, and demonstrates that random augmentation and PFGS both improve ASR performance across Arabic, French, Italian, and Portuguese test sets, with PFGS yielding up to a 19.3% relative WER reduction. The study also shows that filtering reference speech can further lower WER by up to 0.59 points on certain datasets.
arXiv:2603. 29042v2 Announce Type: replace-cross Abstract: Phone recognition (PR) is a key enabler of multilingual and low-resource speech processing tasks, yet robust performance remains elusive.
The paper describes Transsion Speech Team’s submission to Task 1 of the MLC‑SLM 2026 Challenge, aiming at speaker‑attributed transcription for multilingual conversational speech. Their cascaded framework includes a DiariZen‑based speaker diarization module, a Qwen3‑Omni‑based long‑form multilingual ASR module with CTC alignment for precise timestamps, and a fusion module that merges diarization and transcription outputs into speaker‑attributed STM results. On the official evaluation set, the system achieved a tcpMER of 15.41% and secured second place among all participants.
The Qwen-Audio-3.0-ASR Technical Report introduces a Mixture-of-Experts large language model-based automatic speech recognition system that addresses real‑world production challenges such as regional dialects, dynamic entities, hotwords, long‑range context, and disfluent speech. Built on the Qwen backbone and trained on tens of millions of hours of speech data, it supports transcription in 30 languages and 16 Chinese dialects, and offers industry‑domain entity recognition, hierarchical hotword customization, single‑pass polishing, and long‑audio contextual modeling. A streaming variant, Qwen-Audio-3.0-ASR-Streaming, is also presented for low‑latency applications, with evaluations showing state‑of‑the‑art performance against leading commercial systems.
The paper introduces CL-DAF, a Cross‑Lingual Disease‑Alignment Framework that identifies acoustic features whose disease effects are consistent across languages. Using data from 201 English and 75 Bangla speakers, the authors construct a 272‑dimensional acoustic representation and find that 133 features reverse disease direction between languages, leading to poor cross‑lingual transfer (AUC 0.49). By isolating 26 disease‑aligned features, CL‑DAF improves cross‑lingual classification, achieving AUCs of 0.825 (English to Bangla) and 0.722 (Bangla to English).
arXiv:2609.09719v1 Announce Type: new Abstract: Text-aligned speech tokenization methods have emerged to better align speech tokens with LLM token spaces, enabling more effective utilization of pretr...
Text-aligned speech tokenization methods have emerged to better align speech tokens with LLM token spaces, enabling more effective utilization of pretrained LLMs. However, they rely on offline automat...
arXiv:2605. 20712v2 Announce Type: replace-cross Abstract: Automatic speech recognition replaces typing only when correction costs less than manual entry - a threshold determined by error types, not counts: fixing a misrecognized domain term costs far more than inserting a comma.