Data Generation for Speech Recognition based on Generative Adversarial Networks


  • R. Lavanya Assistant Professor, Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur
  • K.B. Kishore Mohan Professor & Head, Department of Bio Medical Engineering, Sri Shanmugha College of Engineering and Technology - [SSCET], Sankari, Salem
  • G. Gomathy HOD, EEE Department, Jaya Engineering College, Chennai-24, Thiruvallur District, Tamil Nadu, Chennai
  • Appana Naga Lakshmi Assistant Professor, Artificial Intelligence, Madanapalle Institute of Technology & Science, Madanapalle, A.P
  • R. Salini Assistant Professor, Department of CSE, Panimalar Engineering College, Chennai


Generative Adversarial Networks, Speech Recognition, Speech Generation, SEGAN


Individuals who are deaf or dumb are likely to derive extra benefit via a speech recognition system that uses GANs. However distracting outside circumstances, individuals will find it straightforward to grasp the information. The speech enhancement approaches prevalent currently work in the frequency domain and/or take the benefit of higher-level elements. Many of them utilize first-order analytics and only solve a restricted set of noise scenarios. Deep networks are being adopted increasingly to get around these drawbacks as a result of their ability to learn challenging tasks from sizable sample datasets. In this paper, a GAN-based strategy is proposed for generating synthetic data for speech emotion recognition. More specifically, we glance into using GANs for collecting the data stream. We examine the implementation of Generative Adversarial Networks (GANs) for trained data enrichment to yield samples for disproportionately represented emotions. The updated specimens demonstrate the recommended model's viability, and appraisals from both specialists and laypeople encourage its efficacy. In doing so, we start looking into generative architectures for voice enhancements, which may gradually comprise more speech-centric design choices to improve their functionality.


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How to Cite

Lavanya, R. ., Mohan, K. K. ., Gomathy, G. ., Lakshmi, A. N. ., & Salini, R. . (2024). Data Generation for Speech Recognition based on Generative Adversarial Networks. International Journal of Intelligent Systems and Applications in Engineering, 12(15s), 126–135. Retrieved from



Research Article