Sensay analyticstm: A real-time speaker-state platform
Andreas Tsiartas, C. Albright, Nikoletta Bassiou, Michael W. Frandsen, I. Miller, Elizabeth Shriberg, Jennifer Smith, L. Lynn Voss
Abstract
Growth in voice-based applications and personalized systems has led to increasing demand for speech- analytics technologies that estimate the state of a speaker from speech. Such systems support a wide range of applications, from more traditional call-center monitoring, to health monitoring, to human-robot interactions, and more. To work seamlessly in real-world contexts, such systems must meet certain requirements, including for speed, customizability, ease of use, robustness, and live integration of both acoustic and lexical cues. This demo introduces SenSay AnalyticsTM, a platform that performs real-time speaker-state classification from spoken audio. SenSay is easily configured and is customizable to new domains, while its underlying architecture offers extensibility and scalability.
BibTeX
@inproceedings{icassp2017_sensayanalyticst,
title = {Sensay analyticstm: A real-time speaker-state platform},
author = {Andreas Tsiartas and C. Albright and Nikoletta Bassiou and Michael W. Frandsen and I. Miller and Elizabeth Shriberg and Jennifer Smith and L. Lynn Voss and Valerie Wagner},
booktitle = {ICASSP 2017},
year = {2017}
}