Sarashina2.2-TTS achieves SOTA kanji reading accuracy via data scaling and Joyo-kanji-targeted synthesis, introduces the Joyo Kanji Yomi Benchmark and Kana-CER metric, and shows stable cross-lingual performance.
CAM++: A fast and efficient network for speaker verification using context- aware masking
9 Pith papers cite this work, alongside 3 external citations. Polarity classification is still indexing.
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UNVERDICTED 9representative citing papers
UniSinger unifies speaker-cloned song generation and accompaniment co-generation SVC in one multimodal diffusion transformer model trained with curriculum learning via task-specific modality masking.
VITA-QinYu is the first expressive end-to-end spoken language model supporting role-playing and singing alongside conversation, trained on 15.8K hours of data and outperforming prior models on expressiveness and conversational benchmarks.
SwanVoice is a zero-shot TTS system for 1-4 speakers that reports higher richness and hierarchy scores than open-source baselines on monologue and dialogue tasks via mixed training and DiffusionNFT post-training.
Omni Parsing framework converts complex multimodal signals into locatable, enumerable, and traceable structured knowledge via hierarchical detection, recognition, and interpreting with strict evidence alignment.
Pmeta-TLA combines a frame-level timbre leakage trigger with meta-learning and PCGrad to inject multiple backdoors into speech models in one training run, claiming better attack success, stealth, and lower cost than baselines.
A singing voice conversion system with boundary-aware information bottleneck and high-frequency augmentation achieves the best naturalness in SVCC2025 subjective tests while using less extra data than competitors.
DRL-CLBA applies DDPG reinforcement learning and deep audio steganography to create sample-specific clean-label backdoor attacks on speech DNNs that resist fine-tuning, pruning, and spectral signature defenses.
MT-BCA-CNN achieves 97% accuracy and 95% F1-score on 27-class few-shot underwater acoustic target recognition by combining channel attention and multi-task learning on the Watkins Marine Life Dataset.
citing papers explorer
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Sarashina2.2-TTS: Tackling Kanji Polyphony in Japanese Speech Generation via Data Scaling and Targeted Data Synthesis
Sarashina2.2-TTS achieves SOTA kanji reading accuracy via data scaling and Joyo-kanji-targeted synthesis, introduces the Joyo Kanji Yomi Benchmark and Kana-CER metric, and shows stable cross-lingual performance.
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Towards Unified Song Generation and Singing Voice Conversion with Accompaniment Co-Generation
UniSinger unifies speaker-cloned song generation and accompaniment co-generation SVC in one multimodal diffusion transformer model trained with curriculum learning via task-specific modality masking.
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VITA-QinYu: Expressive Spoken Language Model for Role-Playing and Singing
VITA-QinYu is the first expressive end-to-end spoken language model supporting role-playing and singing alongside conversation, trained on 15.8K hours of data and outperforming prior models on expressiveness and conversational benchmarks.
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SwanVoice: Expressive Long-Form Zero-Shot Speech Synthesis for Both Monologue and Dialogue
SwanVoice is a zero-shot TTS system for 1-4 speakers that reports higher richness and hierarchy scores than open-source baselines on monologue and dialogue tasks via mixed training and DiffusionNFT post-training.
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Logics-Parsing-Omni Technical Report
Omni Parsing framework converts complex multimodal signals into locatable, enumerable, and traceable structured knowledge via hierarchical detection, recognition, and interpreting with strict evidence alignment.
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Pmeta-TLA: Backdoor Attacks for Speech Classification Models via Meta-Learning with Timbre Leakage Attack
Pmeta-TLA combines a frame-level timbre leakage trigger with meta-learning and PCGrad to inject multiple backdoors into speech models in one training run, claiming better attack success, stealth, and lower cost than baselines.
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Controllable Singing Style Conversion with Boundary-Aware Information Bottleneck
A singing voice conversion system with boundary-aware information bottleneck and high-frequency augmentation achieves the best naturalness in SVCC2025 subjective tests while using less extra data than competitors.
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DRL-CLBA: A Clean Label Backdoor Attack for Speech Classification via DDPG Reinforcement Learning
DRL-CLBA applies DDPG reinforcement learning and deep audio steganography to create sample-specific clean-label backdoor attacks on speech DNNs that resist fine-tuning, pruning, and spectral signature defenses.
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A Multi-task Learning Balanced Attention Convolutional Neural Network Model for Few-shot Underwater Acoustic Target Recognition
MT-BCA-CNN achieves 97% accuracy and 95% F1-score on 27-class few-shot underwater acoustic target recognition by combining channel attention and multi-task learning on the Watkins Marine Life Dataset.