Efficient speaker identification from speech transmitted over Bluetooth networks

This paper studies the process of speaker identification over Bluetooth networks. Bluetooth channel degradations are considered prior to the speaker identification process. The work in this paper employs Mel-frequency cepstral coefficients for feature extraction. Features are extracted from different transforms of the received speech signals such as the discrete cosine transform (DCT), signal plus DCT, discrete sine transform (DST), signal plus DST, discrete wavelet transform (DWT), and signal plus DWT. A neural network classifier is used in the experiments, while the training phase uses clean speech signals and the testing phase uses degraded signals due to communication over the Bluetooth channel. A comparison is carried out between the different methods of feature extraction showing that the DCT achieves the highest recognition rates.