all AI news
Personalization of CTC Speech Recognition Models. (arXiv:2210.09510v1 [cs.CL])
Oct. 19, 2022, 1:17 a.m. | Saket Dingliwal, Monica Sunkara, Srikanth Ronanki, Jeff Farris, Katrin Kirchhoff, Sravan Bodapati
cs.CL updates on arXiv.org arxiv.org
End-to-end speech recognition models trained using joint Connectionist
Temporal Classification (CTC)-Attention loss have gained popularity recently.
In these models, a non-autoregressive CTC decoder is often used at inference
time due to its speed and simplicity. However, such models are hard to
personalize because of their conditional independence assumption that prevents
output tokens from previous time steps to influence future predictions. To
tackle this, we propose a novel two-way approach that first biases the encoder
with attention over a predefined list …
arxiv personalization speech speech recognition speech recognition models
More from arxiv.org / cs.CL updates on arXiv.org
ALBA: Adaptive Language-based Assessments for Mental Health
2 days, 10 hours ago |
arxiv.org
PACE: Improving Prompt with Actor-Critic Editing for Large Language Model
2 days, 10 hours ago |
arxiv.org
Jobs in AI, ML, Big Data
Software Engineer for AI Training Data (School Specific)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Python)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Tier 2)
@ G2i Inc | Remote
Data Engineer
@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania
Artificial Intelligence – Bioinformatic Expert
@ University of Texas Medical Branch | Galveston, TX
Lead Developer (AI)
@ Cere Network | San Francisco, US