33.3492, Books: Respiratory health sensing from speech: Nallanthighal

Wed Nov 9 16:48:42 UTC 2022

LINGUIST List: Vol-33-3492. Wed Nov 09 2022. ISSN: 1069 - 4875.

Subject: 33.3492, Books: Respiratory health sensing from speech: Nallanthighal

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Editor for this issue: Maria Lucero Guillen Puon <luceroguillen at linguistlist.org>
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Date: Wed, 09 Nov 2022 16:47:21
From: Tessa Arneri [lotdissertations-fgw at uva.nl]
Subject: Respiratory health sensing from speech: Nallanthighal

Title: Respiratory health sensing from speech 
Series Title: LOT Dissertation Series  

Publication Year: 2022 
Publisher: Netherlands Graduate School of Linguistics / Landelijke (LOT)
	   http://www.lotpublications.nl/

Book URL: https://www.lotpublications.nl/respiratory-health-sensing-from-speech 

Author: Venkata Srikanth Nallanthighal

Paperback: ISBN:  9789460934087 Pages: 247 Price: Europe EURO 33

Abstract:

Speech production is a complex process involving multiple systems, including
cognitive, muscular, and respiratory systems. Perfect synchrony among these
systems is essential; any lapse in the synchrony would lead to a disorder
manifested in one's speech. Thus, speech is a good pathological indicator.
Respiration is an essential and primary mechanism in speech production. We
first inhale a gulp of air and then produce speech while exhaling. When we run
out of breath, we stop speaking and inhale. Though this process is
involuntary, speech production involves a systematic outflow of air during
exhalation characterized by linguistic content and prosodic characteristics of
the utterance. Modeling the relationship between speech and respiration makes
sensing respiratory dynamics directly from the speech plausible. Modeling such
a relationship is not easy and direct because of the complex nature of speech
and respiration. However, machine learning and deep learning architectures
enable us to model such complex relationships. 

In this thesis, we conduct a comprehensive study to establish the relationship
between speech and respiration. We explore techniques for sensing breathing
signals and breathing parameters from speech using deep learning architectures
and address the challenges involved in establishing the practical purpose of
this technology.

Our main conclusion is that breathing patterns might give us information about
the respiration rate, breathing capacity and thus enable us to understand the
pathological condition of a person using speech conversations. This would help
early and remote diagnosis of various health conditions. Estimating breathing
signal and parameters from the speech signal is an unobtrusive and potentially
cost-effective option for long-term breathing monitoring in telehealthcare
applications.

Linguistic Field(s): Applied Linguistics
                     Computational Linguistics

Written In: English  (eng)

See this book announcement on our website: 
http://linguistlist.org/pubs/books/get-book.cfm?BookID=164353

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