Automatic Speech Recognition System for Low Resource Punjabi Language using Deep Neural Network-Hidden Markov Model (DNN-HMM) - Sobti, Rajni; Guleria, Kalpna; Kadyan, Virender (International Journal of Intelligent Systems and Applications in Engineering, 2024)
In recent years, speech recognition technology has advanced significantly, enabling seamless human-machine interaction. The majority of these advances, however, have focused on major languages with abundant data and resources, neglecting the rich linguistic diversity inherent in low resource languages. There are unique challenges associated with speech recognition in low resource languages, because there is a lack of comprehensive linguistic resources as well as data. To ensure inclusivity and promote global accessibility, researchers recognize the need to bridge this gap. This article focuses on the development of children ASR in the Punjabi languages, along with the potential benefits that can be gained from addressing this understudied field. For the purpose, the speech data from children have been collected (who speak Punjabi) and collected audio data were then segmented using PRAAT (open source software) followed by transcription of segmented audio files. The feature extraction has been implemented using the MFCC algorithm. The process of acoustic modelling has been implemented using various models which include MONO, Tri1, Tri2 and Tri3. The acoustic model then was trained with DNN-HMM to increase the accuracy of the children’s ASR in Punjabi language. The results reveal 83.9% accuracy of children ASR in the Punjabi language. Further, the comparison with the existing models shows that the proposed DNN-HMM model gives better results.