arXiv Machine Learning
Jun 16
Scaling Human and G2P Supervision for Robust Phonetic Transcription
arXiv:2606. 16019v1 Announce Type: cross Abstract: Expert phonetic annotation is costly, especially for non-standard dialects and atypical speech.
Phone Segmentation and Recognition through Phonological Activation Mapping
arXiv:2607. 09020v1 Announce Type: cross Abstract: Phone segmentation and recognition are inherently related tasks, yet modern approaches typically model them separately.
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Hugging Face Trending Papers
Aug 10
Edge Phoneme Recognition for Children's Speech through Age-Aware Training
Detecting phonemes from children's speech has historically been difficult due to the scarcity of training data, and unique characteristics of children's speech. During a phoneme detection competition, we found that training a lightweight model to predict the age of the learner, as well as the phoneme sequence, enabled a 94M-parameter model to outperform WavLM Large models (317M) on the target DrivenData distribution, and fall within approximately 0.
arXiv AI
Aug 12
Edge Phoneme Recognition for Children's Speech through Age-Aware Training
arXiv:2608. 10206v1 Announce Type: new Abstract: Detecting phonemes from children's speech has historically been difficult due to the scarcity of training data, and unique characteristics of children's speech.
arXiv AI
Jun 16
ArtNet: A JEPA-Like Articulatory Predictive Framework for Robust Zero-Shot Phoneme Recognition
arXiv:2606. 16595v1 Announce Type: cross Abstract: Zero-shot cross-lingual phoneme recognition is often hindered by the fragility of direct acoustic-to-symbol mapping, which is susceptible to language-specific variations.
arXiv Machine Learning
Jul 14
An Empirical Recipe for Universal Phone Recognition
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.
arXiv AI
Jul 7
Speaker-Disentangled Chunk-Wise Regression for Syllabic Tokenization
arXiv:2607. 04064v1 Announce Type: cross Abstract: Unsupervised syllabic tokenization aims to learn discrete syllabic tokens that capture latent linguistic content-related structure from raw speech.
arXiv AI
Jun 11
Pretrained self-supervised speech models can recognize unseen consonants
arXiv:2606. 11542v1 Announce Type: cross Abstract: Modern pretrained self-supervised automatic speech recognition models are trained on large-scale audio data to encode speech into contextualized representations.
arXiv AI
Jun 17
Perceptual compensation for tonal context in self-supervised speech models
arXiv:2606. 17835v1 Announce Type: cross Abstract: This study examines the extent to which the wav2vec2.
arXiv Machine Learning
Jun 2
Context-aware child-directed speech detection from long-form recordings
arXiv:2606. 01134v1 Announce Type: cross Abstract: Automatically distinguishing child-directed speech from adult-directed speech in long-form recordings is key to scalable analyses of children's language environments.
arXiv AI
Jul 15
An Omnilingual-ASR-Based Speech-LLM System for the 2nd MLC-SLM Challenge
arXiv:2607. 12468v1 Announce Type: cross Abstract: We describe our submission to Task 1 of the 2nd MLCSLM Challenge: a cascaded diarization-then-recognition system that combines DiariZen-Large-s80 (WavLM-Large) segmentation, CAM++ embedding-based two-speaker clustering, and a LoRA-adapted omniASR LLM 7B v2 recognizer, with no oracle segmentation or speaker labels at test time.
Hugging Face Trending Papers
Jun 24
Does Translation-Enhanced Speech Encoder Pre-training Affect Speech LLMs?
Connecting a pre-trained speech encoder to a Large Language Model (LLM) is the standard architecture for building Speech LLMs. However, a structural misalignment exists between the encoder and the LLM.
arXiv Machine Learning
Jun 30
BabyHuBERT: Multilingual Self-Supervised Learning for Segmenting Speakers in Child-Centered Long-Form Recordings
arXiv:2509. 15001v3 Announce Type: replace-cross Abstract: Child-centered daylong recordings are essential for studying early language development, but existing speech models trained on clean adult data perform poorly due to acoustic and linguistic differences.