Advances in Personalized End-to-End Speech Recognition
Main Points:
- The accuracy of end-to-end speech recognition has greatly improved with advancements in deep learning and automatic speech recognition.
- However, accurately recognizing personal content such as contact names remains a challenge.
- In a new study, researchers propose a personalization solution for an end-to-end system based on connectionist temporal classification.
- The solution utilizes a class-based language model, where a general language model provides context for named entity classes, and personal named entities are stored separately.
Author’s Take:
Advances in deep learning and speech recognition have improved the accuracy of end-to-end speech recognition. However, recognizing personal content like contact names remains a challenge. Researchers have proposed a personalization solution using a class-based language model to improve accuracy in recognizing personal named entities. This could have significant implications in various applications where accurate speech recognition is crucial.