Content Based Audiobooks Indexing using Apache Hadoop Framework

In recent years, content based audio indexing has become the key research area, as the audio content defines the content more precisely and has comparatively subservient density. In this paper, we present conversion of audio books into textual information using CMU SPHINX-4 speech transcriber and efficient indexing of audio books using term frequency inverse document frequency(tf-idf) weights on Apache Hadoop MapReduce framework. In the first phase, audiobook datasets are converted into textual words by training CMU SPHINX 4 speech recognizer with acoustic models. In the next phase, the keywords present in the text file generated from the speech recognizer are filtered using tf-idf weights. Finally, we index audio files based on the keywords extracted from the speech converted text file. As, conversion of speech to text and indexing of audio are space and time intensive tasks, we ported execution of these algorithms on Hadoop MapReduce Framework. Porting content based indexing of audio books on to a Hadoop distributed framework resulted in considerable improvement in time and space utilization. As the amount of data being uploaded and downloaded is escalating, this can be further extended to indexing of image, video and other multimedia forms.