An optimized iterative clustering framework for recognizing speech

In the recent years, many research methodologies are proposed to recognize the spoken language and translate them to text. In this paper, we propose a novel iterative clustering algorithm that makes use of the translated text and reduces error in it. The proposed methodology involves three steps executed over many iterations, namely: (1) unknown word probability assignment, (2) multi-probability normalization, and (3) probability filtering. In the first case, each iteration learns the unknown words from previous iterations and assigns a new probability to the unknown words based on the temporary results obtained in the previous iteration. This process continues until there are no unknown words left. The second case involves normalization of multiple probabilities assigned to a single word by considering neighbour word probabilities. The last step is to eliminate probabilities below the threshold, which ensures the reduction of noise. We measure the quality of clustering with many real-world benchmark datasets. Results show that our optimized algorithm produces more accurate clustering compared to other clustering algorithms.