Personalization of CTC-based End-to-End Speech Recognition Using Pronunciation-Driven Subword Tokenization

Recent advances in deep learning and automatic speech recognition have boosted the accuracy of end-to-end speech recognition to a new level. However, recognition of personal content such as contact names remains a challenge. In this work, we present a personalization solution for an end-to-end system based on connectionist temporal classification. Our solution uses class-based language model, in which a general language model provides modeling of the context for named entity classes, and personal named entities are compiled in a separate finite state transducer. We further introduce a…