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pro vyhledávání: '"Glazer, Neta"'
Automatic Speech Recognition (ASR) technology has made significant progress in recent years, providing accurate transcription across various domains. However, some challenges remain, especially in noisy environments and specialized jargon. In this pa
Externí odkaz:
http://arxiv.org/abs/2406.02649
One of the challenges in applying reinforcement learning in a complex real-world environment lies in providing the agent with a sufficiently detailed reward function. Any misalignment between the reward and the desired behavior can result in unwanted
Externí odkaz:
http://arxiv.org/abs/2402.11367
Autor:
Eitan, Daniel, Pirchi, Menachem, Glazer, Neta, Meital, Shai, Ayach, Gil, Krendel, Gidon, Shamsian, Aviv, Navon, Aviv, Hetz, Gil, Keshet, Joseph
General purpose language models (LMs) encounter difficulties when processing domain-specific jargon and terminology, which are frequently utilized in specialized fields such as medicine or industrial settings. Moreover, they often find it challenging
Externí odkaz:
http://arxiv.org/abs/2310.19708
Open vocabulary keyword spotting is a crucial and challenging task in automatic speech recognition (ASR) that focuses on detecting user-defined keywords within a spoken utterance. Keyword spotting methods commonly map the audio utterance and keyword
Externí odkaz:
http://arxiv.org/abs/2309.08561
Auxiliary learning is an effective method for enhancing the generalization capabilities of trained models, particularly when dealing with small datasets. However, this approach may present several difficulties: (i) optimizing multiple objectives can
Externí odkaz:
http://arxiv.org/abs/2301.13501
Publikováno v:
Oxford Open Immunology; 2022, Vol. 3 Issue 1, p1-15, 15p
