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Do We Need a Reference Signal for Sp...

Manocha, Pranay.

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Do We Need a Reference Signal for Speech Quality Assessment?

Record Type:	Electronic resources : Monograph/item
Title/Author:	Do We Need a Reference Signal for Speech Quality Assessment?/
Author:	Manocha, Pranay.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
Description:	204 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Contained By:	Dissertations Abstracts International85-12B.
Subject:	Computer science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31244365
ISBN:	9798382809045

Do We Need a Reference Signal for Speech Quality Assessment?
Manocha, Pranay.

Do We Need a Reference Signal for Speech Quality Assessment? - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 204 p.

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

Thesis (Ph.D.)--Princeton University, 2024.

This thesis investigates new metrics for assessing speech quality that aim to align more closely with human auditory perception than current methods. It aims to improve the techniques and understanding of speech quality evaluation. It considers traditional methods that compare speech to a perfect (clean) reference and introduces new approaches for scenarios where such a reference is not available. It also emphasizes the significance of reference signals and explores the necessity for flexible evaluation techniques that can function effectively without an ideal reference. The dissertation describes three main categories of metrics: full-reference (FR), no-reference (NR), and non-matching reference (NMR), providing a detailed comparison of their benefits and limitations. Despite the general preference for FR metrics in situations where a corresponding clean reference signal is available, this research identifies specific circumstances where FR metrics may not be the most effective approach, thereby highlighting the utility and relevance of NMR metrics across different evaluative scenarios. Another contribution of this thesis is the introduction of CORN, a novel metric formulated through the integration of FR, and NR metrics. This metric builds on an exhaustive analysis of various evaluation metrics, demonstrating its utility in advancing audio quality assessment. Additionally, applying these methods to spatial audio in augmented and virtual reality settings expands the thesis's contribution to the more general domain of audio quality assessment. This thesis aims to improve the techniques and understanding of speech quality evaluation. This dissertation aims to refine and expand the methodologies and understanding of speech quality evaluation, a crucial step for the evolution of digital communication technologies.

ISBN: 9798382809045Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Audio and speech assessment

Do We Need a Reference Signal for Speech Quality Assessment?
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500 $a Advisor: Finkelstein, Adam.
502 $a Thesis (Ph.D.)--Princeton University, 2024.
520 $a This thesis investigates new metrics for assessing speech quality that aim to align more closely with human auditory perception than current methods. It aims to improve the techniques and understanding of speech quality evaluation. It considers traditional methods that compare speech to a perfect (clean) reference and introduces new approaches for scenarios where such a reference is not available. It also emphasizes the significance of reference signals and explores the necessity for flexible evaluation techniques that can function effectively without an ideal reference. The dissertation describes three main categories of metrics: full-reference (FR), no-reference (NR), and non-matching reference (NMR), providing a detailed comparison of their benefits and limitations. Despite the general preference for FR metrics in situations where a corresponding clean reference signal is available, this research identifies specific circumstances where FR metrics may not be the most effective approach, thereby highlighting the utility and relevance of NMR metrics across different evaluative scenarios. Another contribution of this thesis is the introduction of CORN, a novel metric formulated through the integration of FR, and NR metrics. This metric builds on an exhaustive analysis of various evaluation metrics, demonstrating its utility in advancing audio quality assessment. Additionally, applying these methods to spatial audio in augmented and virtual reality settings expands the thesis's contribution to the more general domain of audio quality assessment. This thesis aims to improve the techniques and understanding of speech quality evaluation. This dissertation aims to refine and expand the methodologies and understanding of speech quality evaluation, a crucial step for the evolution of digital communication technologies.
590 $a School code: 0181.
650 4 $a Computer science. $3 523869
650 4 $a Acoustics. $3 879105
650 4 $a Audiology. $3 537237
653 $a Audio and speech assessment
653 $a Deep learning
653 $a Machine learning
653 $a Perceptual assessment
653 $a Quality assessment
653 $a Speech quality assessment
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690 $a 0800
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