Find in Library

This paper frames a novel methodology for spoken document information retrieval to the spontaneous speech corpora and converting the retrieved document into the corresponding language text. The proposed work involves the three major areas namely spoken keyword detection, spoken document retrieval and automatic speech recognition. The keyword spotting is concerned with the exploit of the distribution capturing capability of the Auto Associative Neural Network (AANN) for spoken keyword detection. It involves sliding a frame-based keyword template along the audio documents and by means of confidence score acquired from the normalized squared error of AANN to search for a match. This work benevolences a new spoken keyword spotting algorithm. Based on the match the spoken documents are retrieved and clustered together. In speech recognition step, the retrieved documents are converted into the corresponding language text using the AANN classifier. The experiments are conducted using the Dravidian language database and the results recommend that the proposed method is promising for retrieving the relevant documents of a spoken query as a key and transform it into the corresponding language.