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.
RAS: a Reliability Oriented Metric for Automatic Speech Recognition
arXiv:2604. 24278v3 Announce Type: replace-cross Abstract: Automatic speech recognition systems often produce confident yet incorrect transcriptions under noisy or ambiguous conditions, which can be misleading for both users and downstream applications.
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arXiv AI
Jun 29
What Was That Again? Certified Robustness for Automatic Speech Recognition
arXiv:2606. 27698v1 Announce Type: cross Abstract: Automatic Speech Recognition systems are notoriously both sensitive to adversarial and benign perturbations.
arXiv AI
Jul 17
RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems
arXiv:2607. 14846v1 Announce Type: cross Abstract: Current voice AI benchmarks typically evaluate isolated capabilities such as speech intelligibility, word error rate, or text-based dialogue quality, but they rarely test whether systems harness the acoustic information that distinguishes spoken language from its textual representation.
arXiv AI
Jun 17
When Multiple Scripts Matter: Evaluating ASR in Clinical Settings
arXiv:2606. 17826v1 Announce Type: cross Abstract: Automatic speech recognition (ASR) in non-English clinical settings is challenged by multiscript variability, where the same term may appear in multiple valid orthographic forms.
arXiv Machine Learning
Jul 14
Generative Testing of Automated Speech Recognition Systems
arXiv:2607. 09833v1 Announce Type: cross Abstract: Automatic speech recognition (ASR) systems have achieved high accuracy with transformer-based models, enabling deployment in critical applications.
arXiv AI
Jun 15
MoDiCoL: A Modular Diagnostic Continual Learning Dataset for Robust Speech Recognition
arXiv:2606. 14459v1 Announce Type: cross Abstract: Modern Automatic Speech Recognition (ASR) systems have made remarkable progress on standard benchmarks, yet performance gaps have emerged under real-world distribution shifts, caused by recording conditions, accents, speech impairments, and noise.
arXiv AI
Jun 18
ASTRA: A Scalable Next-Generation ATCO Training Simulator with Autonomous Simpilots
arXiv:2606. 18319v1 Announce Type: cross Abstract: Air Traffic Control Operators (ATCOs) are vital in ensuring the safe, orderly, and efficient flow of air traffic, yet training capacity is constrained by reliance on specialized human trainers known as simpilots, who must role-play both pilots and ATCOs in a simulated airspace.
arXiv Machine Learning
Jul 3
Towards a Phonology-Informed Evaluation of Multilingual TTS
arXiv:2607. 01965v1 Announce Type: cross Abstract: Neural TTS systems can sound natural across languages, but naturalness does not guarantee the preservation of sound contrasts that distinguish words from their grammatical forms.
arXiv Machine Learning
Jul 21
Precision-Varying Prediction (PVP): Robustifying ASR systems against adversarial attacks
arXiv:2603. 22590v2 Announce Type: replace Abstract: With the increasing deployment of automated and agentic systems, ensuring the adversarial robustness of automatic speech recognition (ASR) models has become highly relevant.
arXiv AI
Jun 18
Something from Nothing: Data Augmentation for Robust Severity Level Estimation of Dysarthric Speech
arXiv:2603. 15988v3 Announce Type: replace-cross Abstract: Dysarthric speech quality assessment (DSQA) is critical for clinical diagnostics and inclusive speech technologies.
Hugging Face Trending Papers
Jul 2
Towards a Phonology-Informed Evaluation of Multilingual TTS
Neural TTS systems can sound natural across languages, but naturalness does not guarantee the preservation of sound contrasts that distinguish words from their grammatical forms. Standard metrics like MOS do not test for this.
arXiv AI
Jun 18
Speaker Verification with Speech-Aware LLMs: Evaluation and Augmentation
arXiv:2603. 10827v2 Announce Type: replace-cross Abstract: Speech-aware large language models (LLMs) can accept speech inputs, yet their training objectives largely emphasize linguistic content or specific fields such as emotions or the speaker's gender, leaving it unclear whether they encode speaker identity.