A collective data generation method for speech language models

Recently we began using Amazon Mechanical Turk (AMT), an Internet marketplace, to deploy our spoken dialogue systems to large audiences for user testing and data collection purposes. This crowdsourcing method of collecting data contrasts with the time- and labor- intensive developer annotation methods. In this paper, we compare these data in various combinations with traditionally-collected corpora for training our speech recognizer's language model. Our results show that AMT text queries are effective for initial language model training for spoken dialogue systems, and that crowd-sourced speech collection within the context of a spoken dialogue framework provides significant improvement.