No abstract

No abstract

This paper presents a robust speaker identification method from degraded speech signals. This proposed method depends on the Mel-frequency cepstral coefficients (MFCCs) for feature extraction from the degraded speech and its discrete cosine transform (DCT). It is known that the MFCCs based speech recognition methods are not robust enough in the presence of noise and channel degradation. So, the feature extraction from the DCT of the signal will assist in achieving a higher recognition rate. The artificial neural network (ANN) classification technique is used in the proposed method. The comparison between the proposed method and the method using the MFCCs only for feature extraction from noisy speech signals and telephone-like degraded signals shows that the proposed method improves the recognition rate in the presence of noise or degradation. , Egypt since 1990. He has published several scientific papers in national and international conferences and journals. His current research areas of interest include image processing, speech processing, digital communications and electromagnetic applications. since 1987. He has published several scientific papers in national and international conferences and journals. His current research areas of interest include adaptive signal processing techniques, image processing, speech processing and digital communications.

No abstract