Summary of the paper

Title A Scalable Architecture For Web Deployment of Spoken Dialogue Systems
Authors Matthew Fuchs, Nikos Tsourakis and Manny Rayner
Abstract We describe a scalable architecture, particularly well-suited to cloud-based computing, which can be used for Web-deployment of spoken dialogue systems. In common with similar platforms, like WAMI and the Nuance Mobile Developer Platform, we use a client/server approach in which speech recognition is carried out on the server side; our architecture, however, differs from these systems in offering considerably more elaborate server-side functionality, based on large-scale grammar-based language processing and generic dialogue management. We describe two substantial applications, built using our framework, which we argue would have been hard to construct in WAMI or NMDP. Finally, we present a series of evaluations carried out using CALL-SLT, a speech translation game, where we contrast performance in Web and desktop versions. Task Error Rate in the Web version is only slightly inferior that in the desktop one, and the average additional latency is under half a second. The software is generally available for research purposes.
Topics Speech Recognition/Understanding, LR Infrastructures and Architectures
Full paper A Scalable Architecture For Web Deployment of Spoken Dialogue Systems
Bibtex @InProceedings{FUCHS12.436,
  author = {Matthew Fuchs and Nikos Tsourakis and Manny Rayner},
  title = {A Scalable Architecture For Web Deployment of Spoken Dialogue Systems},
  booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
  year = {2012},
  month = {may},
  date = {23-25},
  address = {Istanbul, Turkey},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Uğur Doğan and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {978-2-9517408-7-7},
  language = {english}
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