Machine-Aided Methods for Spoken Dialogue System Enhancement and Customization for Call-Center Applications

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 manual effort and expertise. A significant portion of this effort is geared towards the optimization of dialogue call-flow, grammars and prompts of deployed services to reach target key performance indicators (KPIs). We propose a process for spoken dialogue service development, enhancement and customization of deployed services, where data logs are analyzed and used to enhance the service in a semi-automated fashion. A list of mature technologies will be used to: 
  1. identify hot-spots in the dialogue and propose alternative call-flow structures, 
  2. select among list of prompts to reach target KPIs, 
  3. update grammars using transcribed service data and 
  4. customize application for specific user populations. 

Specifically, the list of technologies used will be: affective modeling of spoken dialogue, call-flow/discourse analysis, machine translation, crowd-sourcing, grammar induction, user modeling. The technologies listed above will be integrated in a service-doctoring platform that will enhance deployed services. Our business model is quick deployment of a prototype service, followed by service enhancement using our 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 to SME in the services area with little expertise in speech service development (B2B) and also end-users that are developing their own in house speech service often with limited success (B2C).

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