Speaker Frame Selection for Digital Speech Watermarking

Abstract

Discriminative speaker features vary from one frame to another. Many researcher studied most of the discrimination features for speaker recognition by ignoring phonemic and linguistic information. However, other studies have proposed digital speech watermarking to improve the security of speaker recognition through channel transmission. Although digital speech watermark may be used with speaker information, it can affect speaker information which then can degrade the performance. This paper uses state-of-the-art in frame selection to propose a new way to preserve most of the discriminative features of speaker and to secure speech signals by applying digital speech watermarking technique. In this paper, linear predictive analysis was applied for each frame to extract formants, gain and residual errors. A frequency weighted function was used to quantify formants, and high order correlation as well as error gain is used for weighting the residual errors. The experimental results showed an overall (12 %) effectiveness in terms of performance, time and memory of frame selection technique for speaker recognition and digital speech watermarking.