In this paper, we introduce a new envisioned application for speech which allows users to enter restaurant reviews orally via their mobile device, and, at a later time, update a shared and growing database of consumer-provided information about restaurants. During the intervening period, a speech recognition and NLP based system has analyzed their audio recording both to extract key descriptive phrases and to compute sentiment ratings based on the evidence provided in the audio clip. We report here on our preliminary work moving towards this goal. Our experiments demonstrate that multiaspect sentiment ranking works surprisingly well on speech output, even in the presence of recognition errors. We also present initial experiments on integrated sentence boundary detection and key phrase extraction from recognition output. Index Terms — Speech applications, content creation, sentiment detection, speech summarization, user modelling

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 crowdsourced speech collection within the context of a spoken dialogue framework provides significant improvement. Index Terms — Language models, crowdsourcing, Amazon Mechanical Turk