Latent factor analysis for synthesized speech quality-of-experience assessment

Latent factor analysis for synthesized speech quality-of-experience assessment Text-to-speech (TTS) systems are evolving and making way into numerous commercial systems, such as smartphones and assistive technologies. Notwithstanding, their user perceived quality-of-experience (QoE) is still low compared to natural speech, with distortions arising across numerous perceptual dimensions, such as voice pleasantness, comprehension, and appropriateness of intonation, to name a few. Unfortunately, the effects of such perceptual dimensions on overall perceived QoE is still unknown, particularly across listeners of different genders, thus making it difficult for TTS developers to further improve system quality. To overcome this limitation, this study makes use of exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and model invariance tests to shed light on factors responsible for QoE perception across natural and synthesized speech, as well as male and female listeners. Experimental EFA/CFA results on a publicly available database of commercial TTS systems showed the emergence of two key perceptual dimensions responsible for TTS QoE, namely ‘listening pleasure’ and ‘prosody’. Model invariance tests validated the reliability of the model across male and female listeners, as well as across natural and synthetic voices. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality and User Experience Springer Journals

Latent factor analysis for synthesized speech quality-of-experience assessment

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Publisher
Springer International Publishing
Copyright
Copyright © 2017 by Springer International Publishing Switzerland
Subject
Engineering; Communications Engineering, Networks; User Interfaces and Human Computer Interaction; Behavioral Sciences; Cognitive Psychology; Media Research; Signal,Image and Speech Processing
ISSN
2366-0139
eISSN
2366-0147
D.O.I.
10.1007/s41233-017-0005-6
Publisher site
See Article on Publisher Site

Abstract

Text-to-speech (TTS) systems are evolving and making way into numerous commercial systems, such as smartphones and assistive technologies. Notwithstanding, their user perceived quality-of-experience (QoE) is still low compared to natural speech, with distortions arising across numerous perceptual dimensions, such as voice pleasantness, comprehension, and appropriateness of intonation, to name a few. Unfortunately, the effects of such perceptual dimensions on overall perceived QoE is still unknown, particularly across listeners of different genders, thus making it difficult for TTS developers to further improve system quality. To overcome this limitation, this study makes use of exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and model invariance tests to shed light on factors responsible for QoE perception across natural and synthesized speech, as well as male and female listeners. Experimental EFA/CFA results on a publicly available database of commercial TTS systems showed the emergence of two key perceptual dimensions responsible for TTS QoE, namely ‘listening pleasure’ and ‘prosody’. Model invariance tests validated the reliability of the model across male and female listeners, as well as across natural and synthetic voices.

Journal

Quality and User ExperienceSpringer Journals

Published: Feb 6, 2017

References

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