arXiv Machine Learning
Noise-Robust Forced Alignment for Spontaneous Speech
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv AI
Jul 9
Gradient-Based Speech-to-Text Alignment for Any ASR Model: From CTC to Speech LLMs
arXiv:2607. 06831v1 Announce Type: cross Abstract: Speech-to-text alignment means finding the temporal boundaries of each word in the audio.
arXiv Computation and Language
Oct 9
FA-Bench: A Benchmark for Phone- and Word-Level Timestamp Accuracy in Forced Alignment and ASR on Clean and Noisy Speech
FA-Bench is an open framework that standardizes forced alignment evaluation by fixing transcript normalization, data splits, phone mapping, and scoring procedures. It offers two tracks: one with reference transcripts and another with recognizer outputs, evaluated on clean and four degraded audio conditions using 21 open models and 9 commercial APIs. The benchmark scores every utterance boundary with a tolerance‑based F1 metric, revealing systematic timing biases such as Whisper’s 150 ms early alignment and commercial APIs’ 50 ms late alignment.
arXiv AI
Jul 24
Phonetic forced alignment for low-resource language varieties: Model training and evaluation on Chengdu Mandarin
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.
Hugging Face Trending Papers
Jul 23
Phonetic forced alignment for low-resource language varieties: Model training and evaluation on Chengdu Mandarin
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 Machine Learning
Jul 20
RobustSpeechFlow: Learning Robust Text-to-Speech Trajectories via Augmentation-based Contrastive Flow Matching
arXiv:2605. 22083v2 Announce Type: replace-cross Abstract: While flow-matching text-to-speech (TTS) achieves strong zero-shot speaker similarity and naturalness, it remains susceptible to content fidelity issues, particularly skip and repeat errors from imperfect alignment.
arXiv Computation and Language
Aug 25
Mitigating Speaker Leakage in Cascaded Multi-talker ASR with Diarization-based Transcript Correction
arXiv:2608.22196v1 Announce Type: cross Abstract: While cascaded multi-talker ASR (MT-ASR) leverages state-of-the-art foundation models, its performance is often capped by speaker leakage during sepa...