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

Loading next page...
 
/lp/springer_journal/latent-factor-analysis-for-synthesized-speech-quality-of-experience-ZSQkt0106q
Publisher
Springer Journals
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

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off