Importance of Artificial Intelligence in Neural Network: Speech Signal Segmentation Using K-Means Clustering with Kernelized Deep Belief Networks
Keywords:
speech processing, segmentation, deep learning, K-means C, KDBNAbstract
There has been a tonne of study on use of ML for speech processing applications, particularly voice recognition, over the past few decades. This study suggested an innovative method in speech signal processing and segmentation relied upon deep learning configurations. As input, this speech signal has been accumulated from the crime scene and this signal has been pre-processed using using K-means clustering (K-means C)for cluster the fragments of the input speech signal and process them for noise removal and signal artifacts removal. Here the segmentation is carried out for processed signal using Kernel based deep belief networks (KDBN). Experimental results demonstrate that proposed method outperforms the input speech signal based on both weighted accuracy (WA) and unweighted accuracy (UA).
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Copyright (c) 2023 V. Kakulapati, Gagandeep Singh Gill, Chandramma R., Sambhrant Srivastava, Meenakshi Sharma, Vijay Kumar
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