Word boundary detection for Gujarati speech recognition using in-ear microphone

This paper addresses end point detection algorithm, which is most important and critical part of speech recognition system. Different approaches for endpoint detections are explained and compared to Gujarati words. Speech end point detection is required to find speech segment edge points in the presence of the background noise. It isolates words from the silent portions and improves overall accuracy of the speech recognition system. Because the silence portion is mostly affected by the noises [1]. In this work speech data is collected with the in-ear microphone. Results suggested that it gives a high recognition rate compared to conventional recording methods, i.e. by keeping the mic outside mouth. Word boundary detection is also useful to separate out the different words form sentences. The end point task is quite difficult in real time, because of low signal to noise ratio in most of the situations. Detected words are then passed to the feature extraction block.