Detection and Analysis of Human Emotions through Voice and Speech Pattern Processing

Dasgupta, Poorna Banerjee

doi:10.14445/22312803/ijctt-v52p101

Cited by 34 publications

(15 citation statements)

References 7 publications

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“…Other research has shown that social media (Greyling et al 2019) and online search behavior can be used to monitor specific emotional states (Brodeur et al 2020;Ford et al 2018). Lab research has shown that emotions can be inferred from observation-based obtrusive measures, such speech characteristics (Dasgupta 2017;B. L. Smith et al 1975;Williams and Stevens 1972), combinations of acoustic variables (Banse and Scherer 1996) and voice pitch (Mauss and Robinson 2009).…”

Section: Emotionsmentioning

confidence: 99%

Worker Well-Being: What it Is, and how it Should Be Measured

Wijngaards

King

Burger

et al. 2021

Applied Research Quality Life

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Worker well-being is a hot topic in organizations, consultancy and academia. However, too often, the buzz about worker well-being, enthusiasm for new programs to promote it and interest to research it, have not been accompanied by universal enthusiasm for scientific measurement. Aim to bridge this gap, we address three questions. To address the question ‘What is worker well-being?’, we explain that worker well-being is a multi-facetted concept and that it can be operationalized in a variety of constructs. We propose a four-dimensional taxonomy of worker well-being constructs to illustrate the concept’s complexity and classify ten constructs within this taxonomy. To answer the question ‘How can worker well-being constructs be measured?’, we present two aspects of measures: measure obtrusiveness (i.e., the extent to which obtaining a measure interferes with workers’ experiences) and measure type (i.e., closed question survey, word, behavioral and physiological). We illustrate the diversity of measures across our taxonomy and uncover some hitherto under-appreciated avenues for measuring worker well-being. Finally, we address the question ‘How should a worker well-being measure be selected?’ by discussing conceptual, methodological, practical and ethical considerations when selecting a measure. We summarize these considerations in a short checklist. It is our hope that with this study researchers – working in organizations, in academia or both – will feel more competent to find effective strategies for the measurement worker well-being and eventually make policies and choices with a better understanding of what drives worker well-being.

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Section: Emotionsmentioning

confidence: 99%

Worker Well-Being: What it Is, and how it Should Be Measured

Wijngaards

King

Burger

et al. 2021

Applied Research Quality Life

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show abstract

“…Previous work done has only made attempts to qualitatively and quantitatively detect ASR errors but has not automatically correct these errors as only manual error correction for lexical error has been suggested (Schuller, 2018;Tang et al, 2019). The solution proposed to these aforementioned ASR problem is to build a large targeted dataset for quantifying the detected errors and automatically re-formulate these errors (Dasgupta, 2017;Schuller, 2018;Tang et al, 2019). The term re-formulation in the context of this study means automatically re-adjusting and re-sizing of speaker related errors i.e., user's acoustic irrational behavior during speech communication.…”

Section: Style Variabilitymentioning

confidence: 99%

“…It was then inferred that emotional state has direct influence or alter speech signals based on speech recognition accuracy, classification accuracy and standard deviation parameters. Dasgupta (2017) presented an algorithmic approach for detection of human emotions and quantitative analysis using voice and speech processing through several attributes which are pitch, timbre, loudness and time between words. The approach is based on three different emotional states (normal, angry and panicked) using a low sample data (two speech samples).…”

Section: Related Workmentioning

confidence: 99%

“…This phase consists of different tasks which is related to: Analyze user's characteristics and impediments to performing desired behavior, focusing on heuristic and biases: Heuristics can be defined as simple rules of judgements for information processing to help in surrogating complex decision making problems with easier ones (Lembcke et al, 2019). For this study, the heuristics to be considered based on aforementioned user's irrational behavior are Pitch, Loudness, Timbre (ascend and descend time) and Time between words measured in S, seconds (Dasgupta, 2017 Analyze the strengths/weaknesses of available technology channels and choose the optimal best to carry out the intervention: The appropriate channel to carry out this intervention which is the acoustic nudging is done with the aid of tensor flow application through the speech recognition application.…”

Section: The Analysis Phasementioning

confidence: 99%

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Automatic Re-Formulation of user’s Irrational Behavior in Speech Recognition using Acoustic Nudging Model

Ajayi¹,

Azeta²,

Odun-Ayo³

et al. 2020

Journal of Computer Science

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“…In solitude condition of prosody, the unusual effects of prosody are complicated to reproduce and also the analysing of prosody is difficult due to the function multiplicity [3]. The prosody generation module is the mandatory components of various speech processing software applications specifically businessrelated Text-to-Speech systems today make use of rather unsophisticated systems, characteristically conveying a defaulting sentence accent based on the function word distinction [4].…”

Section: Introductionmentioning

confidence: 99%

Investigation of Pitch and Duration Range in Speech of Sindhi Adults for Prosody Generation Module

Mahar¹,

Hussain²,

Hussain³

et al. 2019

IJACSA

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Prosody refers to structure of sound and rhythm and both are essential parts of speech processing applications. It comprises of tone, stress, intonation and rhythm. Pitch and duration are the core elements of acoustic and that information can make easy to design and development for application module. Through these two peculiarities, the prosody module can be validated. These two factors have been investigated using the sounds of Sindhi adults and presented in this paper. For the experiment and analysis, 245 male and female undergraduate students were selected as speakers belonging from five different districts of upper Sindh and categorized into groups according to their age. Particular sentences were given and recorded individually from the speakers. Afterward, these sentences segmented into words and stored in a database consisting of 1960 sounds. Thus, distance of the frequency in pitch was measured via Standard Deviation (SD). The lowest Mean SD accompanied 0.25Hz and 0.28Hz received from male and female group of district Sukkur. The highest Mean SD has measured with male and female group of district Ghotki along 0.42Hz and 0.49Hz. Generally, the pitch of female's speakers was found high in contrast to male's speaker by 0.072Hz variation.

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Detection and Analysis of Human Emotions through Voice and Speech Pattern Processing

Abstract: Abstract

Cited by 34 publications

References 7 publications

Worker Well-Being: What it Is, and how it Should Be Measured

Worker Well-Being: What it Is, and how it Should Be Measured

Automatic Re-Formulation of user’s Irrational Behavior in Speech Recognition using Acoustic Nudging Model

Investigation of Pitch and Duration Range in Speech of Sindhi Adults for Prosody Generation Module

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