Abstract
Although a lot of research has been done on speaker identification in the presence of noise and channel variation, to the best of our knowledge, no work has been reported for aeronautical applications. In this paper, we aim to fulfill this goal by developing a Speaker Identification System (SIS) for future aeronautical communications systems. Furthermore, we present a novel feature extraction scheme based on multi-resolution analysis. The proposed features called SMFCC use Mel Frequency Cepstral Coefficients (MFCCs) features of stationary wavelet transform sub-bands. The extracted features are modeled using the i-vector approach, and support-vector machines are adopted as a back-end classifier. The performance of the proposed SIS is evaluated using two publicly available databases. Comparison of the proposed approach with the baseline MFCC feature extraction shows the feasibility and the robustness of the proposed method. Besides the noise reduction, the identification accuracy is improved by about 12% at higher signal-to-noise ratios and reaches 97.33% as compared to 88.33% using MFCC for ATCOSIM database.
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