Enhancing Automatic Speech Recognition for Punjabi Dialects: An Experimental Analysis of Incorporating Prosodic Features and Acoustic Variability Mitigation

The development of Automatic Speech Recognition (ASR) systems has varied significantly across the roughly 6500 languages that make up the world's spoken languages. There is a lack of effective ASR systems due to the unavailability of essential resources including high-quality speech corpora, pronunciation dictionaries, and transcriptions, for the low resource languages including the Punjabi language with its several dialects. With a focus on the Malwai, Doabi, Majhi, and Puadhi dialects, this work initiates an experimental investigation of ASR systems developed exclusively for the Punjabi language. To improve speech recognition rates, the study offers a novel method that combines prosodic variables like pitch and probability of voicing (POV) with Mel-frequency cepstral coefficients (MFCC). The goal of this combination of elements is to accurately reproduce the tonal qualities of the Punjabi language. The work addresses the issue of inter-speaker acoustic variabilities using approaches like speaker adaptive training (SAT) and vocal-tract length normalisation (VTLN). The word error rate (WER), which reduces upto 9.25% for the different Punjabi dialects, has been shown to be significantly improvements because of the proposed hybridization of prosodic characteristics. This result highlights how this method may improve ASR systems for tonal languages with limited resources. In addition to demonstrating the value of adding prosodic features and minimising inter-speaker variations, the study advances the field by describing a novel paradigm for creating ASR systems for languages with constrained and sustainable use of resources. This study also presents present state of art as well as emerging trends and a few open innovative problems for research community. Future researchers may carefully observe literature gap to apply this strategy to other low-resource languages and dialects, further demonstrating the effectiveness of the suggested approach.