Speech Emotion Recognition Using LSTM

Registration ID: IJNRD_215659

Published ID: IJNRD2404094

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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: 000122023

Research Area: Computer Science & Technology 

Author Type: Indian Author

Country: Dehradun, Uttarakhand, India

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

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

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