Empirical analysis of linguistic and paralinguistic information for automatic dialect classification

Sinha, Shweta; Jain, Aruna; Agrawal, S.

doi:10.1007/s10462-017-9573-3

Cited by 9 publications

(4 citation statements)

References 33 publications

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“…The fiction play algorithm converts different Q readings each to different players' standings and non-stop player modes. modeling or integrated games where players can learn their Q values from their shared activities known as Joint Action Learner [11][12][13][14].…”

Section: Fictitious Play Algorithmmentioning

confidence: 99%

Reinforcement Learning: A Comprehensive Overview

Makkar

2024

IJIRCST

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Machine Learning is one of the most essential parts of Artificial Intelligence. Machine learning now exists as an important innovation and has a sufficient number of uses. Reinforcement Learning is one of the largest Machine Learning applications that enable machines and software agents to work more precisely and resolve behaviors within a specific context in order to maximize their performance. The self-improvement feature, web-based learning, and minimal effort of Strengthening Learning helped the machines become smart agents in basic technology. With the development of robust and effective algorithms, there is still a lot of work to be done. Therefore, the main purpose of this study is to provide Confirmation Learning reviews and applications using various algorithms from a machine learning perspective.

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Section: Fictitious Play Algorithmmentioning

confidence: 99%

Reinforcement Learning: A Comprehensive Overview

Makkar

2024

IJIRCST

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“…Their findings highlighted that analysis of formant frequencies can contribute to a richer understanding of accent variation. By analyzing acoustic characteristics like formant frequencies and duration in regional dialects of Hindi, Sinha et al (2019) investigated the influence of dialectal variation. Their findings also suggest that with the help of acoustic analysis, it can be postulated as the substance for differentiating and identifying speakers from different dialects.…”

Section: F1 Vs F2 Formant Space Analysis For Vowels Of Hie and Biementioning

confidence: 99%

Identifying acoustic cues for dialect profiling: Policing in multilingual communities of India

Toppo

Sinha

2022

Indonesian J. Appl. Linguist.

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A multilingual country such as India with numerous languages and dialects provides fertile grounds for evasive language crimes. From threat letters to ransom demands, the scope of crime is huge. The cases of illegal immigrants have only added to the fragility of international boundaries especially, during political upheavals. This leads to further vulnerability of society and also creates challenges for the police and law enforcement agencies towards timely intervention. The purpose of the study is to exhibit dialectal variation in Indian English by comparing two varieties. The current paper is based on the acoustic analysis of Indian English spoken by two distinct groups with different mother tongues. Ten native speakers of Hindi and Bangla were recorded in an anechoic chamber. A phonetically balanced passage was selected to be read. The analysis is based on Native Language Influence Detection (Perkins Grant, 2018) to derive acoustic phonetic correlates that can be used as significant identifying markers to distinguish Indian English speakers of Bangla and Hindi speech communities. The paper highlights that dialect profiling in the Indian context can be efficiently correlated with formant frequencies and Voice Onset Time for speech data. Acoustic analysis was done on PRAAT. PRAAT was used in this study because it has often been used by other similar studies to measure desired acoustic parameters simultaneously. Formant frequencies were measured at the midpoint of the vowels in the PRAAT using the LPC formant measurement algorithm. The normalization procedure was applied to the measured formant frequencies of vowels. The research affirms that acoustic analysis can provide verifiable cues for NLID. The framework can be used in the detection of native language influence in speech-centric criminal cases. The acoustic analysis shows that Indian English has subvarieties that could help in dialect profiling. The variation in Indian English vowel patterns could be due to the influence of the native language of the speakers.

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“…It is an established finding that non-verbal emotion communication by prosodic means is more precise when expressors and perceivers are from the same cultural group [ 1 ]. This is because the cultural differences modulate the expression and perception of emotions [ 2 ]. Recent advancements in artificial intelligence have opened up new avenues to study human behavior.…”

Section: Introductionmentioning

confidence: 99%

“…However, perceiving regional dialects of human voice by machine learning is more reliable and less expensive method. Automatic emotion dialect identification refers to the identification of the speaker’s regional dialect by machine learning, inside a predefined language, dependent on the prosodic sign and other phonetic range contained inside the speech signal [ 2 ]. The AI cut shorts the procedure by avoiding the active involvement of human subjects, lengthy procedures, yet giving the results showing the same behavioral pattern.…”

Section: Introductionmentioning

confidence: 99%

Identifying the evidence of speech emotional dialects using artificial intelligence: A cross-cultural study

et al. 2022

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The advancement in technology especially in the field of artificial intelligence has opened up novel and robust ways to reanalyze the many aspects of human emotional behavior. One of such behavioral studies is the cultural impact on the expression and perception of human emotions. In-group advantage makes it easy for the people of the same cultural group to perceive each other’s emotions accurately. The goal of this research is to re-investigate human behavior regarding expression and perception of emotions in speech. The theoretical basis of this research is grounded on the dialect theory of emotions. For the purpose of this study, six datasets of audio speeches have been considered. The participants of these datasets belong to six different cultural areas. A fully automated, machine learning-based framework i.e. Support Vector Machine (SVM) is used to carry out this study. The overall emotion perception for all six cultural groups supports in-group advantage, whereas emotion wise analysis partially supports the In-group advantage.

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Empirical analysis of linguistic and paralinguistic information for automatic dialect classification

Cited by 9 publications

References 33 publications

Reinforcement Learning: A Comprehensive Overview

Reinforcement Learning: A Comprehensive Overview

Identifying acoustic cues for dialect profiling: Policing in multilingual communities of India

Identifying the evidence of speech emotional dialects using artificial intelligence: A cross-cultural study

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