arXiv AI
Aug 21
Towards Quantifying Benchmark Optimization in ASR Models
arXiv:2608. 19936v1 Announce Type: cross Abstract: Public benchmarks are important measures of Automatic Speech Recognition (ASR) model capabilities.
How to Leverage Synthetic Speech for LLM-Based ASR Systems?
arXiv:2606. 29031v1 Announce Type: cross Abstract: In regulated domains such as banking and healthcare, where privacy constraints make real speech costly to collect and retain, synthetic speech from modern text-to-speech (TTS) is an appealing alternative for training automatic speech recognition (ASR) without exposing sensitive customer recordings.
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arXiv Machine Learning
Aug 17
Trajectory Dynamics in Self-Supervised Learning Latent Space for Audio Deepfake Detection
arXiv:2608. 13817v1 Announce Type: cross Abstract: Human speech production is constrained by physiology, giving rise to characteristic temporal structure on acoustic signals.
arXiv Machine Learning
Aug 24
Training DeepFilterNet with Accurate Room Acoustic Simulations Improves Single-Channel Speech Enhancement
The study examines how the realism of synthetic room impulse response (RIR) datasets influences the training of DeepFilterNet3 for single‑channel speech enhancement. By comparing a DNS4 image‑source‑method RIR set with a higher‑fidelity hybrid wave‑based and geometrical acoustics RIR set, the authors find that the more realistic dataset consistently improves objective speech enhancement metrics and significantly reduces ASR word error rates on unseen measured RIRs. The results suggest that overall realism in synthetic acoustic training data enhances DeepFilterNet3’s generalization to new environments.
arXiv Computation and Language
Sep 22
Closing the Speech-Text Gap with Limited Audio for Effective Domain Adaptation in LLM-Based ASR
arXiv:2604.06487v2 Announce Type: replace Abstract: Conventional end-to-end automatic speech recognition (ASR) systems rely on paired speech-text data for domain adaptation. Recent LLM-based ASR arch...
arXiv AI
Jun 6
Beyond Waveform Robustness: Robust Feature-Vocoder Adversarial Attacks on Automatic Speech Recognition
arXiv:2606. 05678v1 Announce Type: cross Abstract: Automatic speech recognition (ASR) systems have become widely used for multilingual speech-to-text transcription.
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 AI
Jun 3
Efficient ASR Training with Conversations that Never Happened
arXiv:2606. 03957v1 Announce Type: cross Abstract: Conversational ASR for lower-resource languages and niche domains is limited by the scarcity of domain-matched multi-speaker training data.
arXiv AI
Jul 14
VoxENES 2026: Benchmarking Generalization of Speech Spoofing Detectors Against LLM-Era TTS and Voice Conversion
arXiv:2607. 11706v1 Announce Type: cross Abstract: Modern LLM-driven text-to-speech (TTS) and voice conversion (VC) systems produce synthetic speech that differs from the generators represented in many legacy spoofing benchmarks.
arXiv Computation and Language
Sep 15
SURE-Voice: A Front-End Baseline for Speech-Evidence Filtering in Speech LLMs
arXiv:2608.27783v3 Announce Type: replace-cross Abstract: Speech language models (speech LLMs) can generate plausible outputs from audio that contains no usable speech evidence. We study this failure...
arXiv Machine Learning
Sep 10
WhisTLE: Deeply Supervised, Text-Only Domain Adaptation for Small Pretrained Speech Recognition Transformers
arXiv:2509.10452v3 Announce Type: replace-cross Abstract: Pretrained automatic speech recognition (ASR) models such as Whisper perform well but still need domain adaptation to handle unseen parlance....
arXiv AI
Aug 20
Aslema at NADI 2026: Augmentation through Fewshot for SLU
Aslema is a system developed for the NADI 2026 Shared Task 5, which involves intent recognition and slot filling. The team evaluated four omni LLMs in a zero‑shot setting and found that fine‑tuned models consistently outperform zero‑shot inference. They further improved performance by augmenting data with culturally grounded Tunisian Derja utterances generated by an LLM and synthetic speech produced via voice cloning, achieving top‑ranked results on the official test set.
arXiv AI
Aug 5
Speech LLMs in Low-Resource Scenarios: Data Volume Requirements and the Impact of Pretraining on High-Resource Languages
arXiv:2508. 05149v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated potential in handling spoken inputs for high-resource languages, reaching state-of-the-art performance in various tasks.