Multiple Feature Extraction for RNN-based Assamese Speech Recognition for Speech to Text Conversion Application

The current work proposes a prototype model for speech recognition in Assamese language using Linear Predictive Coding (LPC) and Mel frequency cepstral coefficient (MFCC). The speech recognition is a part of a speech to text conversion system. The LPC and MFCC features are extracted by two different Recurrent Neural Networks (RNN), which are used to recognize the vocal extract of Assamese language- a major language in the North Eastern part of India. In this work, decision block is designed by a combined framework of RNN block to extract the features. Using this combined architecture our system is able to generate 10% gain in the recognition rate than the case when individual architectures are used.