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Akademický článek
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Akademický článek
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Autor:
Liu, Yu-Tung, Wang, Kuan-Chen, Chao, Rong, Siniscalchi, Sabato Marco, Yeh, Ping-Cheng, Tsao, Yu
Surface electromyography (sEMG) recordings can be contaminated by electrocardiogram (ECG) signals when the monitored muscle is closed to the heart. Traditional signal-processing-based approaches, such as high-pass filtering and template subtraction,
Externí odkaz:
http://arxiv.org/abs/2411.18902
Autor:
Devi Sacchetto
Publikováno v:
Anuac, Vol 11, Iss 1 (2022)
Recensione di Massimiliano Mollona, Cristina Papa, Veronica Redini, Valeria Siniscalchi, Antropologia delle imprese: Lavoro, reti, merci, Roma, Carocci, 2021, pp. 220.
Externí odkaz:
https://doaj.org/article/5be339aa4bfc4a459da137716467caea
In this work, we propose a novel consistency-preserving loss function for recovering the phase information in the context of phase reconstruction (PR) and speech enhancement (SE). Different from conventional techniques that directly estimate the phas
Externí odkaz:
http://arxiv.org/abs/2409.16282
This work investigates two strategies for zero-shot non-intrusive speech assessment leveraging large language models. First, we explore the audio analysis capabilities of GPT-4o. Second, we propose GPT-Whisper, which uses Whisper as an audio-to-text
Externí odkaz:
http://arxiv.org/abs/2409.09914
Autor:
Yang, Chao-Han Huck, Park, Taejin, Gong, Yuan, Li, Yuanchao, Chen, Zhehuai, Lin, Yen-Ting, Chen, Chen, Hu, Yuchen, Dhawan, Kunal, Żelasko, Piotr, Zhang, Chao, Chen, Yun-Nung, Tsao, Yu, Balam, Jagadeesh, Ginsburg, Boris, Siniscalchi, Sabato Marco, Chng, Eng Siong, Bell, Peter, Lai, Catherine, Watanabe, Shinji, Stolcke, Andreas
Given recent advances in generative AI technology, a key question is how large language models (LLMs) can enhance acoustic modeling tasks using text decoding results from a frozen, pretrained automatic speech recognition (ASR) model. To explore new c
Externí odkaz:
http://arxiv.org/abs/2409.09785
Autor:
Khan, Muhammad Salman, La Quatra, Moreno, Hung, Kuo-Hsuan, Fu, Szu-Wei, Siniscalchi, Sabato Marco, Tsao, Yu
Self-supervised representation learning (SSL) has attained SOTA results on several downstream speech tasks, but SSL-based speech enhancement (SE) solutions still lag behind. To address this issue, we exploit three main ideas: (i) Transformer-based ma
Externí odkaz:
http://arxiv.org/abs/2408.04773
Autor:
La Quatra, Moreno, Turco, Maria Francesca, Svendsen, Torbjørn, Salvi, Giampiero, Orozco-Arroyave, Juan Rafael, Siniscalchi, Sabato Marco
This work is concerned with devising a robust Parkinson's (PD) disease detector from speech in real-world operating conditions using (i) foundational models, and (ii) speech enhancement (SE) methods. To this end, we first fine-tune several foundation
Externí odkaz:
http://arxiv.org/abs/2406.16128
Italy exhibits rich linguistic diversity across its territory due to the distinct regional languages spoken in different areas. Recent advances in self-supervised learning provide new opportunities to analyze Italy's linguistic varieties using speech
Externí odkaz:
http://arxiv.org/abs/2406.15862