Abstract
Automatic Speech Recognition (ASR) is a self-governing, computer-based spoken language transcript for real-time applications. It is used in various real time applications and it listens the speech signals through a microphone, identifies the words, and assists a network in converting the written text. When we use the ASR system in multiple environments there is a possibility of ambient noise captured by a microphone unit and ASR system doesn't predicting correct words. The Non-linear Acoustic Noise Cancellation (NANC) approach based automatic speech recognition method focused on the properties of non-linear sound noise cancellation. There are several distinct small segments in this approach, such as speech signal sounds, syllables, and so on. As an acrylic symbol associated with organs, these units analyze syllables to find acoustic properties of speech signals. This experimental study has adopted Convolutional Neural Network (CNN) based noise reduction in the speech recognition system with an accuracy of 98.5%. Finally, a speech signal has been identified through the ASR's vocabulary, which has been obtained with correct words after all phonetic signs are present.