• Anurag shekhar
• Heena patil
• Nripesh Vatsa
• Supriyo Ghosh

Speech recognition technology has made significant strides in recent years, but its application for speech-impaired children remains limited. These children often struggle to communicate effectively due to various speech disorders, which present unique challenges for traditional speech recognition systems. This research explores the application of deep learning algorithms to develop a more robust and accurate speech recognition model tailored to the specific needs of speech-impaired children. By leveraging advanced neural network architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer models, the study aims to enhance the system's ability to recognize and interpret the diverse speech patterns associated with different speech impairments. The research involves collecting a comprehensive dataset of speech samples from children with various speech disorders, followed by rigorous preprocessing to address noise and variability in the data. The deep learning models are then trained and validated using this dataset, with a focus on optimizing performance through techniques like cross-validation and hyper parameter tuning. Results indicate that the proposed deep learning-based system significantly outperforms traditional speech recognition systems in terms of accuracy, precision, and recall when applied to speech-impaired children. The study also highlights the potential for integrating this technology into speech therapy practices, providing a valuable tool for educators and therapists working with speech-impaired children. While the research presents promising results, it also acknowledges the need for further refinement and testing to address remaining challenges. This work underscores the importance of developing specialized technological solutions for vulnerable populations and opens up new avenues for research and application in the field of speech recognition and therapy.

Automatic speech recognition, Deep learning, Deep Neural Networks, Natural Language Processing, Systematic Review.

"UTILIZING DEEP LEARNING TECHNIQUES FOR RECOGNIZING SPEECH IN CHILDREN WITH SPEECH IMPAIRMENTS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 8, page no.e895-e902, August-2024, Available :http://www.jetir.org/papers/JETIR2408495.pdf

"UTILIZING DEEP LEARNING TECHNIQUES FOR RECOGNIZING SPEECH IN CHILDREN WITH SPEECH IMPAIRMENTS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 8, page no. ppe895-e902, August-2024, Available at : http://www.jetir.org/papers/JETIR2408495.pdf