Self Determining Speaker Recognition by Three Level Segmental Processing Of Linear Prediction Residual

This paper proposes a speaker specific source information at different levels.speaker recognition system exploits the source information (LP residual) present at different levels namely subsegmental, segmental &suprasegmental. The subsegmental analysis considers LP residual in blocks of 5 msec with shift of 2.5 msec to extract speaker information. The segmental analysis extracts speaker information by processing in blocks of 20 msec with shift of 2.5 msec. The suprasegmental speaker information is extracted by viewing in blocks of 250 msec with shift of 6.25 msec. The speaker recognizer studies performed using TIMIT (Texas Instruments and Massachusetts Institute of Technology) databases demonstrate that the segmental analysis provides best performance followed by subsegmental analysis. The suprasegmental analysis gives the least performance. However, the evidences from all the three levels of processing seem to be different and combine well to provide improved performance, demonstrating different speaker information captured at each level of processing. Finally, the combined evidence from all the three levels of processing together with vocal tract information further improves the speaker recognition performance.