Sentiment Analysis on Speech Data using MFCC

Registration ID: IJNRD_221716

Published ID: IJNRD2407252

DOI: Click Here to Get

Aditya Pratap Mall , Arpita Singh , Ananya Gupta , Aman Upadhyay , Aparana Sharma

Sentiment Analysis, Speech Recognition, MFCC, DTW, Natural Language Processing.

Sentiment analysis is a popular technique these days used to detect emotions or sentiments from the audio. Lot of people have done research in this field bringing out most of the features. Mostly the work is done in speech recognition using the polarities. The functionality can be extended to a plugin system where people can analyze and assess the sentiments of the audience during online meetings and webinars. Here, we tried to work on all most common type of emotions through the best sufficient algorithm. Sentiment analysis gained more recognition over the past years, and largely focused with text mining techniques around sentiment analysis . However, the research community is still very new to audio sentiment analysis. We implement sentiment analysis on speech transcripts of different speakers in this research to detect emotions of speaker involved in the conversation so it can further be implemented as plugin by various application platforms.

Published Paper   E-Certificate

Aditya Pratap Mall, Arpita Singh, Ananya Gupta, Aman Upadhyay, & Aparana Sharma (July-2024). Sentiment Analysis on Speech Data using MFCC . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(7), c472-c479. https://ijnrd.org/papers/IJNRD2407252.pdf

Volume 9 Issue 7, July-2024

Pages : c472-c479

Paper Reg. ID: IJNRD_221716

Published Paper Id: IJNRD2407252

Downloads: 000121977

Research Area: Computer Science & Technology 

Country: Ghaziabad, Uttar Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2407252.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2407252

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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