One of the current research areas is speech recognition by aiding in the recognition of speech signals through computer applications. In this research paper, Acoustic Nudging, (AN) Model is used in re-formulating the persistence automatic speech recognition (ASR) errors that involves user’s acoustic irrational behavior which alters speech recognition accuracy. GMM helped in addressing low-resourced attribute of Yorùbá language to achieve better accuracy and system performance. From the simulated results given, it is observed that proposed Acoustic Nudging-based Gaussian Mixture Model (ANGM) improves accuracy and system performance which is evaluated based on Word Recognition Rate (WRR) and Word Error Rate (WER)given by validation accuracy, testing accuracy, and training accuracy. The evaluation results for the mean WRR accuracy achieved for the ANGM model is 95.277% and the mean Word Error Rate (WER) is 4.723%when compared to existing models. This approach thereby reduce error rate by 1.1%, 0.5%, 0.8%, 0.3%, and 1.4% when compared with other models. Therefore this work was able to discover a foundation for advancing current understanding of under-resourced languages and at the same time, development of accurate and precise model for speech recognition.