Native Language Recognition using Bidirectional Long Short-Term Memory Network

Abstract

Speech Recognition of native language is the process of recognizing the language of a client dependent on the speech or content writing in another language. This article proposes the utilization of spectrogram as well as on cochleagram-oriented concepts separated from extremely short speech expressions (0.8 s by and large) to deduce the local language of the speaking person. The bidirectional long short-term memory (BLSTM) neural systems are received to classify the expressions between the local dialects. A lot of analyses is completed for the system engineering look and the framework's precision is assessed on the approval informational index. By and large precision is accomplished utilizing the Mel-recurrence Cepstral coefficients (MRCC) and Gammatone Recurrence Cepstral Coefficients (GRCC), separately. In addition, the advanced MFCC oriented BLSTM system and GFCC based BLSTM systems are combined to make use of their features. The examinations demonstrate that the execution of the combined system outperforms the individual BLSTM systems and precision of 75.69% is accomplished on the assessment information.