PISDD: Development of a Pashto Isolated Spoken Digit Dataset and ASR System Using Hybrid Deep Learning Models

Automatic speech recognition (ASR) not only enables hands-free use of various electronic gadgets but also facilitates the creation of print-ready dictation. Despite significant advancements in the field of ASR, low-resource languages such as Pashto still face challenges in achieving comparable maturity. While some efforts have been reported in the literature, the lack of publically available datasets and robust models for Pashto ASR remains a significant barrier. This study aims to address these challenges by developing a novel Pashto isolated spoken digit dataset (PISDD) and proposing a hybrid deep learning-based ASR system. The PISDD is developed by first collecting audio data from 500 native speakers of Pashto, followed by preprocessing techniques including denoising, and partitioning each speaker data into ten digits. This individual digit is then converted into 2D histogram images using the Mel-frequency cepstral coefficient (MFCC) feature extraction technique. A hybrid convolutional neural network (CNN) and bidirectional long short-term memory (BLSTM) model is proposed for the classification, which takes this 2D histogram image as input and predict the output digit. The model uses a three-layer CNN for spatial feature extraction, followed by two BLSTM layers for temporal feature modeling. The proposed model achieves an accuracy of 96.42% on the 30% test set, outperforming the two baseline models: CNN and convolutional recurrent neural network (C-RNN), trained on the same dataset, which achieved 94.12% and 95.23% accuracy, respectively.