Distributed speaker recognition

Speech recognition systems are gaining increasing importance with the wide-spread use of mobile and portable devices and other interactive voice response systems. Because of the resource constraints on such devices and the requirements of specific applications, the need to perform speech recognition over a data network becomes inevitable. The requirements of such a system with a human at one end and a machine at the other end are clearly asymmetric. The major focus of this work is to enable speaker recognition for information access over the network. Assuming that at the client end the device is either a Personal Digital Assistant(PDA) or a cellphone, an attempt is made to perform part of computation at the client end, thus conserve bandwidth. Experiments have been performed on both TIMIT data and TIMIT data passed through a speech codec. The results indicate that by performing feature extraction at the client end, the bitrate can be reduced significantly to 13.6kbps with 96% recognition performance.