Spoken Dialogue Analytics (SpeDial)

The speech services industry has been growing both for telephony applications and, recently, also for smartphones (e.g., Siri). Despite recent progress in spoken dialogue system (SDS) technologies the development cycle of speech services still requires significant effort and expertise. A significant portion of this effort is geared towards the development of the domain semantics and associated grammars, system prompts and spoken dialogue call-flow. We propose a semi-automated process for spoken dialogue service development and speech service enhancement of deployed services, where incoming speech service data are semi-automatically transcribed and analyzed (human-in-the-loop).

A list of mature technologies will be used to:

1) identify hot-spots in the dialogue and propose alternative call-flow structures,

2) mine for relevant data to enhance grammars and

3) mine for relevant data to update service prompts.

Specifically the technologies used will be: grammar induction, text-mining for language modeling, affective modeling of speech and text data, machine translation, crowd-sourcing, speech recognition/transcription, ontology induction. The technologies will be integrated in a service doctoring platform that will enhance deployed services using the human-in-the-loop paradigm. Our business model is quick deployment of a prototype service, followed by service enhancement using our semi-automated service doctoring platform. The reduced development time and time-to-market will provide significant differentiation for SME in the speech services areas, as well as end-users. The business opportunity is significant especially given the consolidation of the speech services industry and the lack of major competition. Our offering is attractive for SME in the services area with little expertise in speech service development (B2B) and also end-user that are developing their own in house speech service often with limited success (B2C).