Utilizing Deep Learning Techniques for the Classification of Spoken Languages in India - Patel, Priyesha; Falke, Ayushi; Waghela, Dipen; Vishwa, Shah (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024)
In Western countries, speech-recognition applications are accepted. In East Asia, it isn’t as common. The complexity of the language might be one of the main reasons for this latency. Furthermore, multilingual nations such as India must be considered in order to achieve language recognition (words and phrases) utilizing speech signals. In the last decade, experts have been clamoring for more study on speech. In the initial part of the pre-processing step, a pitch and audio feature extraction technique were used, followed by a deep learning classification method, to properly identify the spoken language. Various feature extraction approaches will be discussed in this review, along with their advantages and disadvantages. Also discussed were the distinctions between various machine learning and deep learning approaches. Finally, it will point the way for future study in Indian spoken language recognition, as well as AI technology.