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Learning to Determine Who is the Better Speaker

  • Speech can be more or less likable in various ways and comparing speakers by likability has important applications such as speaker selection or matching. Determining the likability of a speaker is a difficult task which can be simplified by breaking it down into pairwise preference decisions. Using a corpus of 5440 pairwise preference ratings collected previously through crowd-sourcing, we train classifiers to determine which of two speakers is “better”. We find that modeling the speech feature sequences using LSTMs outperforms conventional methods that pre-aggregate feature averages by a large margin, indicating that the prosodic structure should be taken into account when determining speech quality. Our classifier reaches an accuracy of 97 % for coarse-grained decisions, where differences between speech quality in both stimuli is relatively large.

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Metadaten
Author:Timo BaumannORCiDGND
DOI:https://doi.org/10.21437/SpeechProsody.2018-165
Parent Title (English):Proceedings of the 9th International Conference on Speech Prosody, 13-16 June 2018, Poznań, Poland
Publisher:ISCA
Document Type:conference proceeding (article)
Language:English
Year of first Publication:2018
Release Date:2022/06/27
First Page:819
Last Page:822
Institutes:Fakultät Informatik und Mathematik
Begutachtungsstatus:peer-reviewed
Publication:Externe Publikationen
research focus:Information und Kommunikation
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG