Personalization of CTC-based End-to-End Speech Recognition Using
  Pronunciation-Driven Subword Tokenization
Zhihong Lei Ernest Pusateri Shiyi Han Leo Liu Mingbin Xu Tim Ng Ruchir Travadi Youyuan Zhang Mirko Hannemann Man-Hung Siu Zhen Huang
Recent advances in deep learning and automatic speech recognition have
improved the accuracy of end-to-end speech recognition systems, but recognition
of personal content such as contact names remains a challenge. In this work, we
describe our personalization solution for an end-to-end speech recognition
system based on connectionist temporal classification. Building on previous
work, we present a novel method for generating additional subword tokenizations
for personal entities from their pronunciations. We show that using this
technique in combination with two established techniques, contextual biasing
and wordpiece prior normalization, we are able to achieve personal named entity
accuracy on par with a competitive hybrid system.