Abstract:
This research is concerned with the
development of speech recognition front-end for
segmenting and clustering continuous Bangla
speech sentence to some predefined clusters.
From the study of different previous research
works it was observed that the front-end is an
important part of any speech recognition system.
In our work, the original speech sentences were
recorded and stored as RIFF (.wav) file format.
Then a segmentation approach was used to
segment the continuous speech into uniquely
identifiable and meaningful units. Among the
different techniques, the word/sub-word
segmentation is simple and produces very good
results. This is why this technique was selected for
speech segmentation to obtain improved
performance. After segmentation, the segmented
words were clustered into different clusters
according to the number of syllables and the sizes
of the segmented words. The test database
contained 758 words/sub-words segmented from
120 sentences. Each sentence was recorded from
six different speakers and saved as a different
wave file. The developed system achieved the
segmentation accuracy rate at about 95%.
