Description:
<jats:title>Abstract</jats:title>
<jats:p>Speech recognition of continuous speech will be greatly influenced by the word segmentation process of the speech input. Proper segmentation will result in better speech recognition. This study proposed automatic speech recognition by applying local adaptive thresholding in the segmentation process. The segmentation method used is an enhanced blocking block area method whose input is a spectrogram image of the speech signal. While the locally adaptive thresholding method used is the Niblack method which is the best method compared to other methods, namely Sauvola, Bradley, Guanglei Xiong, and Bernsen when applied to the enhanced blocking block area method. For the speech recognition process, using Mel-frequency cepstral coefficients (MFCC) as a feature extraction method and Hidden Markov Model (HMM) as speech recognition. The experimental results show that by using 400 sentences consisting of 80 testing data and 320 as training data and using K-fold cross-validation, the highest accuracy is 60,8%. This result has no significant difference with the use of global thresholding in the segmentation process.</jats:p>