Speech Emotion Recognition Using LSTM

Registration ID: IJNRD_215659

Published ID: IJNRD2404094

DOI: Click Here to Get

Rakshit Upadhyay , Yash Khati , Saurabh Kaperwan , Aditya Verma

Speech emotion recognition is a demanding task in modern day system applications. It is an important research topic that is used to improve public health and contribute towards the ongoing progress of healthcare technology. In current time there are requirements of applications which can work specific task by giving voice commands like Alexa, Google Assistant, Cortana, Siri. But these applications do not recognize human emotion and engage with them. One of the difficult tasks in Speech emotion recognition is to obtain emotion features effectively from user’s voice. There has been much research in the field of SER including the use of acoustic and temporal and deep learning models. There has been conducted a lot of research on traditional machine learning algorithms like Support Vector Machine (SVM) [1], K- Nearest Neighbor (KNN) [2], Convolutional Neural Network (CNN) [3], Graph Neural Networks (GNN) [4]. An SER system targets thespeaker’s existence by extracting and classifying the prominent features from a preprocessed speech signal. Some primary human emotions are anger, neutral, happiness and sadness, which define the emotional state of human at a particular time which can be classified using trained intelligent system. The improve emotionrecognition accuracy we use features of user voice like pitch, speech intensity and Mel-frequency cepstral coefficients. (MFCC) [5]. Throughout the past ten years, the determination of speech signals emotions was a primary focus but the enhancing the present effectiveness in recognizing needs is imperative, considering the significant dearth of understanding surrounding the fundamental temporal connections inherent in the speech waveform. To fully use the change in emotional content over phase, a new method to voice recognition is now being recommended, integrating structured audio data with Long Short-Term Memory (LSTM) [6] networks. The temporal aspects of the time series were augmented by extracting structural speech features from the waves, now responsible for preserving the intrinsic connections between layers within the actual speech. Many optimized techniques based on LSTM are provided to ascertain emotional concentration across multiple blocks. At the beginning, the approach minimizes computing expenses by altering the traditional forgetting gate. Secondly, instead of relying on the output from the previous iteration of the conventional method, an attention mechanism is used on both the time and feature dimensions within the LSTM’s final output. Instead of depending on outcomes from the previous stage, an efficient technique has been used to find the spatial and characteristic aspects in the final output of the LSTM. SER has broad potential in the field of human- computer interaction,healthcare to track The emotional state of patient, providing best user experience through intelligent call centers and bankingsector.

Published Paper   E-Certificate

Rakshit Upadhyay, Yash Khati, Saurabh Kaperwan, & Aditya Verma (April-2024). Speech Emotion Recognition Using LSTM. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(4), a781-a786. https://ijnrd.org/papers/IJNRD2404094.pdf

Volume 9 Issue 4, April-2024

Pages : a781-a786

Paper Reg. ID: IJNRD_215659

Published Paper Id: IJNRD2404094

Downloads: 000121989

Research Area: Computer Science & Technology 

Country: Dehradun, Uttarakhand, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2404094.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2404094

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

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

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave | IJNRD.ORG | IJNRD.COM | IJPUB.ORG

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

Call For Paper - Volume 10 | Issue 10 | October 2025

IJNRD is a Scholarly Open Access, Peer-reviewed, and Refereed Journal with a High Impact Factor of 8.76 (calculated by Google Scholar & Semantic Scholar | AI-Powered Research Tool). It is a Multidisciplinary, Monthly, Low-Cost Journal that follows UGC CARE 2025 Peer-Reviewed Journal Policy norms, Scopus journal standards, and Transparent Peer Review practices to ensure quality and credibility. IJNRD provides indexing in all major databases & metadata repositories, a citation generator, and Digital Object Identifier (DOI) for every published article with full open-access visibility.

The INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to advance applied, theoretical, and experimental research across diverse fields. Its goal is to promote global scientific information exchange among researchers, developers, engineers, academicians, and practitioners. IJNRD serves as a platform where educators and professionals can share research evidence, models of best practice, and innovative ideas, contributing to academic growth and industry relevance.

Indexing Coverage includes Google Scholar, SSRN, ResearcherID-Publons, Semantic Scholar (AI-Powered Research Tool), Microsoft Academic, Academia.edu, arXiv.org, ResearchGate, CiteSeerX, ResearcherID (Thomson Reuters), Mendeley, DocStoc, ISSUU, Scribd, and many more recognized academic repositories.

How to submit the paper?

By Our website

Click Here to Submit Paper Online

By Mail

Send your paper to editor@ijnrd.org

Important Dates for Current issue

Paper Submission Open For: October 2025

Current Issue: Volume 10 | Issue 10 | October 2025

Impact Factor: 8.76

Last Date for Paper Submission: Till 31-Oct-2025

Notification of Review Result: Within 1-2 Days after Submitting paper.

Publication of Paper: Within 01-02 Days after Submititng documents.

Frequency: Monthly (12 issue Annually).

Journal Type: IJNRD is an International Peer-reviewed, Refereed, and Open Access Journal with Transparent Peer Review as per the new UGC CARE 2025 guidelines, offering low-cost multidisciplinary publication with Crossref DOI and global indexing.

Subject Category: Research Area

Call for Paper: More Details