Double ended speech enabled system in Indian travel & tourism industry

Abstract

In this research paper we emphasized on development of double ended voice enabled system in order to receive the voice query and convey voiced output message related to travel and tourism domain in Indian language. The voice enable system was developed using multiple components such as automatic speech recognizer (ASR) engine, query classifier and speech synthesis engine. The speech recognition engine plays very crucial role in speech based system which we have evaluated using multiple pattern recognition algorithms namely Hidden Markov Model (HMM), Support Vector Machine (SVM), ontology based feed forward back propagation neural network (OFFBPNN), dynamic time warping (DTW). The performance of SVM AND HMM were seen superior with respect to OFFBPNN, DTW which were measured in terms of word accuracy and word error rate. The output of ASR is fed to k-nearest neighbour (KNN) query classifier and the end result of classifier is finally passed to Odia speech synthesizer to deliver the response in voice mode. We have employed voice transformation technique in speech synthesis system to produce the spoken output in male, female, child and robotic voice. The developed double ended voice enabled system is operational over Odia spoken query and delivered the response in synthesized Odia voice.