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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Speech Emotion Recognition Using LSTM |
| Authors Name: | Rakshit Upadhyay , Yash Khati , Saurabh Kaperwan , Aditya Verma |
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IJNRD_215659
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| Published Paper Id: | IJNRD2404094 |
| Published In: | Volume 9 Issue 4, April-2024 |
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| Abstract: | 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. |
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| Cite Article: | "Speech Emotion Recognition Using LSTM", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.a781-a786, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404094.pdf |
| Downloads: | 00033 |
| 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 |
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Published Paper ID:IJNRD2404094 Registration ID: 215659 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: a781-a786 Country: Dehradun, Uttarakhand, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2404094 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2404094 |
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