AutoSSR: an efficient approach for automatic spontaneous speech recognition model for the Punjabi Language

In this article, the authors have presented the design and development of automatic spontaneous speech recognition of the Punjabi language. To dimensions up to the natural speech recognizer, the very large vocabulary Punjabi text corpus has been taken from a Punjabi interview’s speech corpus, presentations, etc. Afterward, the Punjabi text corpus has been cleaned by using the proposed corpus optimization algorithm. The proposed automatic spontaneous speech model has been trained with 13,218 of Punjabi words and more than 200 min of recorded speech. The research work also confirmed that the 2,073,456 unique in-word Punjabi tri-phoneme combinations present in the dictionary comprise of 131 phonemes. The performance of the proposed model has grown increasingly to 87.10% sentence-level accuracy for 2381 Punjabi trained sentences and word-level accuracy of 94.19% for 13,218 Punjabi words. Simultaneously, the word error rate has been reduced to 5.8% for 13,218 Punjabi words. The performance of the proposed system has also been tested by using other parameters such as overall likelihood per frame and convergence ratio on various iterations for different Gaussian mixtures.