Speech Emotion Recognition Using LSTM and MFCC features

9 pages•Published: August 6, 2024

Arun Kari, Nandhini Muthuraman and Sasirekha R

Abstract

Speech is utilized in human-machine connection and serves as a signal of human involvement. The Speech Emotion Recognition (SER) system is a novel form of this interactive system. Sufficient intelligence is provided by the SER to facilitate effective human-computer interaction. Based on the speaker's words, the SER system classifies emotions into groups such as "neutral," "calm," "happy," "sad," "angry," "fearful," "disgust," and "surprise." Languages and machine learning models suitable for SER are defined in this paper. Deep learning is used by this system to effectively classify and learn from multidimensional data. Primary results for a system using the LSTM algorithm and MFCC feature tools are also presented in this work. For the simplicity of user engagement, we have then implemented this model as a website through the usage of a third party.

Keyphrases: long short term memory, machine learning, mel frequency cepstrum coefficient, speech emotion recognition.

In: Rajakumar G (editor). Proceedings of 6th International Conference on Smart Systems and Inventive Technology, vol 19, pages 172-180.

Links:	https://easychair.org/publications/paper/8zgn
	https://doi.org/10.29007/jrmb

BibTeX entry

@inproceedings{ICSSIT2024:Speech_Emotion_Recognition_Using,
  author    = {Arun Kari and Nandhini Muthuraman and Sasirekha R},
  title     = {Speech Emotion Recognition Using LSTM and MFCC features},
  booktitle = {Proceedings of 6th International Conference on Smart Systems and Inventive Technology},
  editor    = {Rajakumar G},
  series    = {Kalpa Publications in Computing},
  volume    = {19},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2515-1762},
  url       = {/publications/paper/8zgn},
  doi       = {10.29007/jrmb},
  pages     = {172-180},
  year      = {2024}}

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