8th International Conference on Spoken Language Processing

Jeju Island, Korea
October 4-8, 2004

Unsupervised Learning from Users' Error Correction in Speech Dictation

Dong Yu (1), Mei-Yuh Hwang (2), Peter Mau (1), Alex Acero (1), Li Deng (1)

(1)Microsoft Research, USA
(2) University of Washington, USA

We propose an approach to adapting automatic speech recognition systems used in dictation systems through unsupervised learning from users' error correction. Three steps are involved in the adaptation: 1) infer whether the user is correcting a speech recognition error or simply editing the text, 2) infer what the most possible cause of the error is, and 3) adapt the system accordingly. To adapt the system effectively, we introduce an enhanced two-pass pronunciation learning algorithm that utilizes the output from both an n-gram phoneme recognizer and a Letter-to-Sound component. Our experiments show that we can obtain greater than 10% relative word error rate reduction using the approaches we proposed. Learning new words gives the largest performance gain while adapting pronunciations and using a cache language model also produce a small gain.

Full Paper

Bibliographic reference.  Yu, Dong / Hwang, Mei-Yuh / Mau, Peter / Acero, Alex / Deng, Li (2004): "Unsupervised learning from users' error correction in speech dictation", In INTERSPEECH-2004, 1969-1972.