Skip navigation
Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01df65vc22d
Title: Do we need a Reference Signal for Speech Quality Assessment?
Authors: Manocha, Pranay
Advisors: Finkelstein, Adam
Contributors: Computer Science Department
Keywords: Audio and Speech Assessment
Deep Learning
Machine Learning
Perceptual Assessment
Quality Assessment
Speech Quality Assessment
Subjects: Computer science
Acoustics
Audiology
Issue Date: 2024
Publisher: Princeton, NJ : Princeton University
Abstract: This thesis investigates new metrics for assessing speech quality that aim to alignmore 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.
URI: http://arks.princeton.edu/ark:/88435/dsp01df65vc22d
Type of Material: Academic dissertations (Ph.D.)
Language: en
Appears in Collections:Computer Science

Files in This Item:
File Description SizeFormat 
Manocha_princeton_0181D_14974.pdf6.68 MBAdobe PDFView/Download


Items in Dataspace are protected by copyright, with all rights reserved, unless otherwise indicated.