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Main Title: Prediction of Dialogue Success with Spectral and Rhythm Acoustic Features Using DNNS and SVMS
Author(s): Lykartsis, Athanasios
Kotti, Margarita
Papangelis, Alexandros
Stylianou, Yannis
Type: Conference Object
Is Part Of: 10.14279/depositonce-9530
Language Code: en
Abstract: In this paper we investigate the novel use of exclusively audio to predict whether a spoken dialogue will be successful or not, both in a subjective and in an objective manner. To achieve that, multiple spectral and rhythmic features are inputted to support vector machines and deep neural networks. We report results on data from 3267 spoken dialogues, using both the full user response as well as parts of it. Experiments show an average accuracy of 74% can be achieved using just 5 acoustic features, when analysing merely 1 user turn, which allows both a real-time but also a fairly accurate prediction of a dialogue successfulness only after one short interaction unit. From the features tested, those related to speech rate, signal energy and cepstrum are amongst the most informative. Results presented here outperform the state of the art in spoken dialogue success prediction through solely acoustic features.
Issue Date: 14-Feb-2019
Date Available: 24-Feb-2020
DDC Class: 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
780 Musik
Subject(s): dialogue success
acoustic features
deep neural networks
support vector machines
Proceedings Title: 2018 IEEE Spoken Language Technology Workshop (SLT)
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Publisher Place: New York, NY
Publisher DOI: 10.1109/SLT.2018.8639580
ISBN: 978-1-5386-4334-1
Notes: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Appears in Collections:FG Audiokommunikation » Publications

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