Expressive Speech Identifications based on Hidden Markov Model

Lebai Lutfi, Syaheerah Binti and Montero Martínez, Juan Manuel and Barra Chicote, Roberto and Lucas Cuesta, Juan Manuel and Gallardo Antolín, Ascensión (2009). Expressive Speech Identifications based on Hidden Markov Model. In: "Proceedings of the International Conference on Health Informatics, HEALTHINF 2009", 14/01/2009 - 17/01/2009, Porto, Portugal. ISBN 978-3-642-11720-6.

Description

Title: Expressive Speech Identifications based on Hidden Markov Model
Author/s:
  • Lebai Lutfi, Syaheerah Binti
  • Montero Martínez, Juan Manuel
  • Barra Chicote, Roberto
  • Lucas Cuesta, Juan Manuel
  • Gallardo Antolín, Ascensión
Item Type: Presentation at Congress or Conference (Article)
Event Title: Proceedings of the International Conference on Health Informatics, HEALTHINF 2009
Event Dates: 14/01/2009 - 17/01/2009
Event Location: Porto, Portugal
Title of Book: Proceedings of the Second International Conference on Health Informatics, HEALTHINF 2009
Date: 2009
ISBN: 978-3-642-11720-6
Volume: 52
Subjects:
Freetext Keywords: Affective computing, Biometrics, Speech processing, Emotion identification.
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Ingeniería Electrónica
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

This paper concerns a sub-area of a larger research field of Affective Computing, focusing on the employment of affect-recognition systems using speech modality. It is proposed that speech-based affect identification systems could play an important role as next generation biometric identification systems that are aimed at determining a person’s ‘state of mind’, or psycho-physiological state. The possible areas for the deployment of voice-affect recognition technology are discussed. Additionally, the experiments and results for emotion identification in speech based on a Hidden Markov Models (HMMs) classifier are also presented. The result from experiment suggests that certain speech feature is more precise to identify certain emotional state, and that happiness is the most difficult emotion to detect.

More information

Item ID: 5576
DC Identifier: http://oa.upm.es/5576/
OAI Identifier: oai:oa.upm.es:5576
Official URL: http://www.healthinf.biostec.org/HEALTHINF2009/index.htm
Deposited by: Memoria Investigacion
Deposited on: 22 Dec 2010 12:19
Last Modified: 20 Apr 2016 14:21
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