Introduces a feature-vocoder adversarial attack on ASR using SSL representations that reports +26.6 WER black-box transfer and +36.2 WER defense resistance over baselines.
Nisqa: A deep cnn-self-attention model for multidimensional speech quality prediction with crowdsourced datasets.arXiv preprint arXiv:2104.09494
8 Pith papers cite this work. Polarity classification is still indexing.
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VABench is a new multi-dimensional benchmark for evaluating synchronous audio-video generation across text-to-AV, image-to-AV, and stereo tasks.
Emo-LiPO applies listwise preference optimization to model global emotion intensity ordering in LLM TTS, yielding better accuracy and controllability than supervised or DPO baselines on a new multi-speaker dataset.
JASTIN is an instruction-driven audio evaluation system that achieves state-of-the-art correlation with human ratings on speech, sound, music, and out-of-domain tasks without task-specific retraining.
A Conformer-conditioned decoder-only language model generates discrete tokens via a neural audio codec to separate four music stems, reaching near state-of-the-art perceptual quality and top NISQA on vocals in MUSDB18-HQ tests.
Feature-aligned watermarking embeds a codec-generated pseudo-speech signal into the spectrogram to raise robustness against reconstruction models while keeping imperceptibility comparable to prior methods.
Voice range indicates TTS model capability with VITS highest, Glow-TTS best at soft phonation, and CPPs of 7-8 dB marking natural quality while values over 10 dB sound robotic.
citing papers explorer
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Beyond Waveform Robustness: Robust Feature-Vocoder Adversarial Attacks on Automatic Speech Recognition
Introduces a feature-vocoder adversarial attack on ASR using SSL representations that reports +26.6 WER black-box transfer and +36.2 WER defense resistance over baselines.
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VABench: A Comprehensive Benchmark for Audio-Video Generation
VABench is a new multi-dimensional benchmark for evaluating synchronous audio-video generation across text-to-AV, image-to-AV, and stereo tasks.
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Emo-LiPO: Listwise Preference Optimization for Fine-Grained Emotion Intensity Control in LLM-based Text-to-Speech
Emo-LiPO applies listwise preference optimization to model global emotion intensity ordering in LLM TTS, yielding better accuracy and controllability than supervised or DPO baselines on a new multi-speaker dataset.
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JASTIN: Aligning LLMs for Zero-Shot Audio and Speech Evaluation via Natural Language Instructions
JASTIN is an instruction-driven audio evaluation system that achieves state-of-the-art correlation with human ratings on speech, sound, music, and out-of-domain tasks without task-specific retraining.
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Discrete Token Modeling for Multi-Stem Music Source Separation with Language Models
A Conformer-conditioned decoder-only language model generates discrete tokens via a neural audio codec to separate four music stems, reaching near state-of-the-art perceptual quality and top NISQA on vocals in MUSDB18-HQ tests.
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Feature-Aligned Speech Watermarking for Robustness to Reconstruction Distortions
Feature-aligned watermarking embeds a codec-generated pseudo-speech signal into the spectrogram to raise robustness against reconstruction models while keeping imperceptibility comparable to prior methods.
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Voice Mapping of Text-to-Speech Systems: A Metric-Based Approach for Voice Quality Assessment
Voice range indicates TTS model capability with VITS highest, Glow-TTS best at soft phonation, and CPPs of 7-8 dB marking natural quality while values over 10 dB sound robotic.
- A Survey of Advancing Audio Super-Resolution and Bandwidth Extension from Discriminative to Generative Models