Spoken Language Recognization Based on Features and Classification Methods

Abstract

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. Purpose of this research is to Learn transfer learning approaches like Alexnet, VGGNet, and ResNet & CNN etc. using CNN model we got best accuracy for Language Recognition.