Fix it where it fails: Pronunciation learning by mining error corrections from speech logs
Zhenzhen Kou, Daisy Stanton, Fuchun Peng, Françoise Beaufays, Trevor Strohman · 2015
The pronunciation dictionary, or lexicon, is an essential component in an automatic speech recognition (ASR) system in that incorrect pronunciations cause systematic misrecognitions. It typically consists of a list of word-pronunciation pairs written by linguists, and a grapheme-to-phoneme (G2P) engine to generate pronunciations for words not in the list. The hand-generated list can never keep pace with the growing vocabulary of a live speech recognition system, and the G2P is usually of limited accuracy. This is especially true for proper names whose pronunciations may be influenced by various historical or foreign-origin factors. In this paper, we propose a language-independent approach to detect misrecognitions and their corrections from voice search logs. We learn previously unknown pronunciations from this data, and demonstrate that they significantly improve the quality of a production-quality speech recognition system.