Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages

Autor:	Zhang, Yu, Han, Wei, Qin, James, Wang, Yongqiang, Bapna, Ankur, Chen, Zhehuai, Chen, Nanxin, Li, Bo, Axelrod, Vera, Wang, Gary, Meng, Zhong, Hu, Ke, Rosenberg, Andrew, Prabhavalkar, Rohit, Park, Daniel S., Haghani, Parisa, Riesa, Jason, Perng, Ginger, Soltau, Hagen, Strohman, Trevor, Ramabhadran, Bhuvana, Sainath, Tara, Moreno, Pedro, Chiu, Chung-Cheng, Schalkwyk, Johan, Beaufays, Françoise, Wu, Yonghui
Rok vydání:	2023
Předmět:	Computer Science - Computation and Language Computer Science - Sound Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	We introduce the Universal Speech Model (USM), a single large model that performs automatic speech recognition (ASR) across 100+ languages. This is achieved by pre-training the encoder of the model on a large unlabeled multilingual dataset of 12 million (M) hours spanning over 300 languages, and fine-tuning on a smaller labeled dataset. We use multilingual pre-training with random-projection quantization and speech-text modality matching to achieve state-of-the-art performance on downstream multilingual ASR and speech-to-text translation tasks. We also demonstrate that despite using a labeled training set 1/7-th the size of that used for the Whisper model, our model exhibits comparable or better performance on both in-domain and out-of-domain speech recognition tasks across many languages. Comment: 20 pages, 7 figures, 8 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2303.01037 Zobrazit plný text záznamu View this record from Arxiv