Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Toyin, Hawau"'
Autor:
Vayani, Ashmal, Dissanayake, Dinura, Watawana, Hasindri, Ahsan, Noor, Sasikumar, Nevasini, Thawakar, Omkar, Ademtew, Henok Biadglign, Hmaiti, Yahya, Kumar, Amandeep, Kuckreja, Kartik, Maslych, Mykola, Ghallabi, Wafa Al, Mihaylov, Mihail, Qin, Chao, Shaker, Abdelrahman M, Zhang, Mike, Ihsani, Mahardika Krisna, Esplana, Amiel, Gokani, Monil, Mirkin, Shachar, Singh, Harsh, Srivastava, Ashay, Hamerlik, Endre, Izzati, Fathinah Asma, Maani, Fadillah Adamsyah, Cavada, Sebastian, Chim, Jenny, Gupta, Rohit, Manjunath, Sanjay, Zhumakhanova, Kamila, Rabevohitra, Feno Heriniaina, Amirudin, Azril, Ridzuan, Muhammad, Kareem, Daniya, More, Ketan, Li, Kunyang, Shakya, Pramesh, Saad, Muhammad, Ghasemaghaei, Amirpouya, Djanibekov, Amirbek, Azizov, Dilshod, Jankovic, Branislava, Bhatia, Naman, Cabrera, Alvaro, Obando-Ceron, Johan, Otieno, Olympiah, Farestam, Fabian, Rabbani, Muztoba, Baliah, Sanoojan, Sanjeev, Santosh, Shtanchaev, Abduragim, Fatima, Maheen, Nguyen, Thao, Kareem, Amrin, Aremu, Toluwani, Xavier, Nathan, Bhatkal, Amit, Toyin, Hawau, Chadha, Aman, Cholakkal, Hisham, Anwer, Rao Muhammad, Felsberg, Michael, Laaksonen, Jorma, Solorio, Thamar, Choudhury, Monojit, Laptev, Ivan, Shah, Mubarak, Khan, Salman, Khan, Fahad
Existing Large Multimodal Models (LMMs) generally focus on only a few regions and languages. As LMMs continue to improve, it is increasingly important to ensure they understand cultural contexts, respect local sensitivities, and support low-resource
Externí odkaz:
http://arxiv.org/abs/2411.16508
Autor:
Djanibekov, Amirbek, Toyin, Hawau Olamide, Alshalan, Raghad, Alitr, Abdullah, Aldarmaki, Hanan
Developing robust automatic speech recognition (ASR) systems for Arabic, a language characterized by its rich dialectal diversity and often considered a low-resource language in speech technology, demands effective strategies to manage its complexity
Externí odkaz:
http://arxiv.org/abs/2411.05872
Speech recognition and speech synthesis models are typically trained separately, each with its own set of learning objectives, training data, and model parameters, resulting in two distinct large networks. We propose a parameter-efficient approach to
Externí odkaz:
http://arxiv.org/abs/2410.18607
Autor:
Doughman, Jad, Afzal, Osama Mohammed, Toyin, Hawau Olamide, Shehata, Shady, Nakov, Preslav, Talat, Zeerak
Recent improvements in the quality of the generations by large language models have spurred research into identifying machine-generated text. Systems proposed for the task often achieve high performance. However, humans and machines can produce text
Externí odkaz:
http://arxiv.org/abs/2406.11073
We present ArTST, a pre-trained Arabic text and speech transformer for supporting open-source speech technologies for the Arabic language. The model architecture follows the unified-modal framework, SpeechT5, that was recently released for English, a
Externí odkaz:
http://arxiv.org/abs/2310.16621