Speaker identification and its application in automobile industry for automatic seat adjustment.

In this paper, an application of speaker identification in automobile industry is proposed. The work is divided into two main categories. The first part deals with the task of speaker identification where a system is trained and tested for multiple users using a database of isolated Hindi digits and Hindi sentences. A hybrid new algorithm is used for speaker identification which captures the benefits of both LPC and MFCC feature extraction technique. The new proposed technique shows an improvement of 2.05% over conventional MFCC features for isolated Hindi digits and 12.41% for Hindi sentences. It also shows an improvement of 53.26 over LPC for Hindi sentence and 32.51% for isolated Hindi digit over LPC. The proposed features were also tested for real time noisy environment by adding speech and F16 noise to test voice samples with varying degree of distortion starting from 0 to 20 dB. The second part describes the interfacing techniques and design of the hardware configuration for seat adjustment. The proposed model is designed using MATLAB. Speech samples from users are recorded through a microphone. Different features of this wav file are evaluated and fed into the model generated during testing phase. Depending on outcome from the classifier, a user is identified. Once the user is successfully identified, signals are sent to the servo motor through arduino microcontroller interfaced through MATLAB to automatically adjust the driver's seat. [ABSTRACT FROM AUTHOR]