Training LVCSR Systems on Thousands of Hours of Data
G. Evermann, Ho Yin Chan, Mark Gales, Bill Zong Jia, David Mrva, Philip C. Woodland, Kai Yu · 2006
Typical systems for large vocabulary conversational speech recognition (LVCSR) have been trained on a few hundred hours of carefully transcribed acoustic training data. The paper describes an LVCSR system for the conversational telephone speech (CTS) task trained on more than 2000 hours of data for which only approximate transcriptions were available. The challenges of dealing with such a large data set and the accuracy improvements over the small baseline system are discussed. The effect on both acoustic and language modelling performance is studied. Overall, increasing the training data size from 360 h to 2200 h and optimising the training procedure reduced the word error rate on the DARPA/NIST 2003 evaluation set by about 20% relative.