Convolution neural network based Speech Emotion Recognition
Shalini Singhal
, Meenakshi Nawal , Vipin Jain , Anurish Gangrade
convolution neural network, MFCC, speech emotion recognition
Automatic speech emotion recognition has grown in popularity because it allows for natural human-computer connection. One way to recognize emotion is voice. Speech, however, also includes silence that cannot be related to emotion. The elimination of silence and/or ignoring silence while paying greater attention to the segment of speech is two ways to improve performance. This Paper propose a combination of silence elimination and a care model in this paper to enhance the performance of speech emotion. An improved CNN model is presented here which consists of combination of convolution 1d layers and generalized to form a 9 layer architecture of CNN (convolutional neural network), model accuracy has been checked with respect to emotion classes such as considering 5 emotions considered as angry, calm, fearful, happy, sad for male as well as female, likewise included use of classes such as positive, negative, neutral to achieve optimum accuracy. The results show that silence cancellation and attention model combinations are better than just the noise cancellation model or just the attention model. In the realm of human-computer interaction, speech emotion recognition is a critical and difficult job. Various models and feature sets for training the system have been proposed in previous work. Using input signals of various lengths, a novel speech-emotion detection system based on Convolutional Neural Networks (CNN) is presented in this research. With the use of a powerful GPU, a model is created and fed with unprocessed speech from a specified dataset for training, classification, and testing purposes. Finally, it achieves a convincing accuracy of 89.00%, which is far higher than any other comparable job on this dataset. This work will have an impact on the creation of social and conversational robots that can convey all the subtleties of human emotion.In terms of accuracy of the model the results are comparatively improved as compared to previous models using same dataset.
"Convolution neural network based Speech Emotion Recognition", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.9, Issue 1, page no.491 - 497, January-2024, Available :https://ijsdr.org/papers/IJSDR2401072.pdf
Volume 9
Issue 1,
January-2024
Pages : 491 - 497
Paper Reg. ID: IJSDR_209874
Published Paper Id: IJSDR2401072
Downloads: 000347232
Research Area: Science & Technology
Country: Jaipur, Rajasthan, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
