Unsupervised training techniques for natural language call routing

Developing a speech application requires collecting and manually transcribing many hours of task-specific training. In recent years, unsupervised training approaches, which automatically transcribe task-dependent audio and train speech and language models using these automatic transcriptions, have reduced dependence on manual transcriptions. In this paper, we leverage our state-of-the-art speech recognition technology and use automatic transcriptions to reduce time and manual effort in developing a call routing application. Two key differentiators of our work include using different recognition strategies for unsupervised training vs. call routing, and investigating the impact of unsupervised training on call routing accuracy.