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Training regimes for Emotion and Sentiment Analysis

Retrieving Speech Samples with Similar Emotional Content Using a Triplet Loss Function


The ability to identify speech with similar emotional content is valuable to many applications, including speech retrieval, surveillance, and emotional speech synthesis. While current formulations in speech emotion recognition based on classification or regression are not appropriate for this task, solutions based on preference learning offer appealing approaches for this task. This paper aims to find speech samples that are emotionally similar to an anchor speech sample provided as a query. This novel formulation opens interesting research questions.

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Authors:
Reza Lotfian, Carlos Busso
Submitted On:
9 May 2019 - 12:32pm
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[1] Reza Lotfian, Carlos Busso, "Retrieving Speech Samples with Similar Emotional Content Using a Triplet Loss Function", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4222. Accessed: Jun. 20, 2019.
@article{4222-19,
url = {http://sigport.org/4222},
author = {Reza Lotfian; Carlos Busso },
publisher = {IEEE SigPort},
title = {Retrieving Speech Samples with Similar Emotional Content Using a Triplet Loss Function},
year = {2019} }
TY - EJOUR
T1 - Retrieving Speech Samples with Similar Emotional Content Using a Triplet Loss Function
AU - Reza Lotfian; Carlos Busso
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4222
ER -
Reza Lotfian, Carlos Busso. (2019). Retrieving Speech Samples with Similar Emotional Content Using a Triplet Loss Function. IEEE SigPort. http://sigport.org/4222
Reza Lotfian, Carlos Busso, 2019. Retrieving Speech Samples with Similar Emotional Content Using a Triplet Loss Function. Available at: http://sigport.org/4222.
Reza Lotfian, Carlos Busso. (2019). "Retrieving Speech Samples with Similar Emotional Content Using a Triplet Loss Function." Web.
1. Reza Lotfian, Carlos Busso. Retrieving Speech Samples with Similar Emotional Content Using a Triplet Loss Function [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4222