Dialect recognition for low resource language using an adaptive filter bank

Abstract

Dialect recognition of low resource languages is the next stage in the technological advancement in speech recognition. Traditional methods for dialects recognition such as mel frequency cesptral coefficients (MFCC) and discrete wavelet transform (DWT) work better for high resource languages, however, the performance is low when applied in low resource languages. This paper presents a new approach for Pashto dialects recognition using an adaptive filter bank with MFCC and DWT. In this approach, features are extracted using adaptive filter bank in MFCC and DWT followed by classification through hidden Markov model (HMM), support vector machines (SVM) and K-nearest neighbors (KNN). The results obtained from the proposed method are very satisfactory with an overall dialect recognition accuracy of [Formula: see text].