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Prediction and Enhancement of Speech...

Edraki, Amin,

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Prediction and Enhancement of Speech Intelligibility in Challenging Acoustic Environments /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Prediction and Enhancement of Speech Intelligibility in Challenging Acoustic Environments // Amin Edraki.
Author:	Edraki, Amin,
Description:	1 electronic resource (180 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-10, Section: B.
Contained By:	Dissertations Abstracts International86-10B.
Subject:	Listening. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31923450
ISBN:	9798311909006

Prediction and Enhancement of Speech Intelligibility in Challenging Acoustic Environments /
Edraki, Amin,

Prediction and Enhancement of Speech Intelligibility in Challenging Acoustic Environments /Amin Edraki. - 1 electronic resource (180 pages)

Source: Dissertations Abstracts International, Volume: 86-10, Section: B.

Speech intelligibility refers to the portion of a spoken message correctly identified by a listener, making it a critical perceptual attribute in challenging acoustic environments, such as low signal-to-noise ratios. This work addresses two key issues in speech intelligibility: prediction and enhancement. In the Speech Intelligibility Prediction (SIP) segment, we introduce two state-of-the-art, reference-based SIP algorithms that utilize Spectro-Temporal Modulation (STM) analysis of input speech. We present a data-driven, interpretable STM weighting function that assigns varying importance to different STM frequencies, with findings contextualized through psychoacoustic modulation transfer functions. Our algorithms achieved state-of-the-art performance across multiple unseen test datasets and under diverse distortion and processing conditions. Additionally, we propose a probabilistic linguistic augmentation method to modify existing SIP algorithms, incorporating the linguistic predictability of sentences. Using next-word probabilities from a pre-trained language model, we estimate contextual predictability for SIP, yielding improved intelligibility across datasets with varying levels of linguistic predictability.For speech enhancement under challenging acoustic conditions, we investigate bone-conducted speech. Bone-conducted speech captures speech signals transmitted through bone vibration rather than through the air, offering a resilient alternative in noisy environments. This thesis explores personalized enhancement approaches using deep neural networks to improve the intelligibility and quality of bone-conducted speech, with a focus on speaker adaptation.

English

ISBN: 9798311909006Subjects--Topical Terms:

530843
Listening.

Prediction and Enhancement of Speech Intelligibility in Challenging Acoustic Environments /
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500 $a Advisors: Chan, Wai-Yip; Jensen, Jesper; Fogerty, Daniel.
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520 $a Speech intelligibility refers to the portion of a spoken message correctly identified by a listener, making it a critical perceptual attribute in challenging acoustic environments, such as low signal-to-noise ratios. This work addresses two key issues in speech intelligibility: prediction and enhancement. In the Speech Intelligibility Prediction (SIP) segment, we introduce two state-of-the-art, reference-based SIP algorithms that utilize Spectro-Temporal Modulation (STM) analysis of input speech. We present a data-driven, interpretable STM weighting function that assigns varying importance to different STM frequencies, with findings contextualized through psychoacoustic modulation transfer functions. Our algorithms achieved state-of-the-art performance across multiple unseen test datasets and under diverse distortion and processing conditions. Additionally, we propose a probabilistic linguistic augmentation method to modify existing SIP algorithms, incorporating the linguistic predictability of sentences. Using next-word probabilities from a pre-trained language model, we estimate contextual predictability for SIP, yielding improved intelligibility across datasets with varying levels of linguistic predictability.For speech enhancement under challenging acoustic conditions, we investigate bone-conducted speech. Bone-conducted speech captures speech signals transmitted through bone vibration rather than through the air, offering a resilient alternative in noisy environments. This thesis explores personalized enhancement approaches using deep neural networks to improve the intelligibility and quality of bone-conducted speech, with a focus on speaker adaptation.
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650 4 $a Linguistics. $3 524476
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650 4 $a Signal to noise ratio. $3 3681178
650 4 $a Acoustics. $3 879105
650 4 $a Speech. $3 530580
650 4 $a Voice recognition. $3 3564741
650 4 $a Signal processing. $3 533904
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710 2 $a Queen's University (Canada). $e degree granting institution. $3 3765623
720 1 $a Chan, Wai-Yip $e degree supervisor.
720 1 $a Jensen, Jesper $e degree supervisor.
720 1 $a Fogerty, Daniel $e degree supervisor.
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