An innovative approach for assessing partial speech problems is the automatic speech recognition system. It is a technology is used for significant quantity of naturally occurring speech from speakers with disabilities which can be subjected to linguistic and acoustical study. Kannada, an important Indian dialect, is the focus of the present study. ASR offers a variety of strategies based on the situation. The isolated word ASR system for the Kannada language is presented in the proposed study using artificial neural networks. Words that are spoken can be converted into the matching written formats using isolated word recognition. The properties of a speech stream are distinguished using Mel Frequency Cepstrum and Linear Predictive Coding (LPC). The objective of this study is to create a standalone word recognizer utilizing a word acoustic model and a combination of LPC and ANN. The proposed system consists of the two stages they are training and testing phases. 50 isolated words are recorded utilizing multiple speakers in a silent environment as part of the training process. There were both individuals with partial speech disorders and normal speakers of all ages and genders among individuals. The features of the resulting sample are extracted using LPC and trained using ANN after each word is repeated several times by the particular speaker. During the testing phase, the system receives input of isolated word utterances, LPC coefficients are determined, and recognition is done using the sigmoid function and text that corresponds to the stated word. The system was trained and tested using 50 isolated words from the Kannada language. The system results had a 95.8% accuracy rate for the combined population of normal speakers and people with partially speech disorders.