The NiFe 2 O 4 /nitrogen doped carbon composite was synthesized via calcination of NiFe-MOF in a N 2 atmosphere. Nitrogen-doped carbon not only improves the conductivity of the carbon-based material, but also provides pathway, allowing Li + diffusion rapidly during the charge and discharge processes. The electrochemical data reveal that NiFe 2 O 4 /nitrogen-doped carbon nanocomposite delivers a capacity as high as 760 mAh•g −1 at 0.2 C after 50 cycles, and show good rate performance by remaining a capacity of 600 mAh•g −1 even at 1 C. The excellent electrochemical characteristics might be contributed to the NiFe 2 O 4 ordered nanorod structure, well-marked porosity, and as well as the composition of nitrogen-doped carbon.

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Unseen handset mismatch and limited training/test data are the major source of performance degradation for speaker identification in telecommunication environment. In this paper, a vector quantization (VQ)-based prosody modeling and an eigen-prosody analysis (EPA) is integrated to transform the close-set speaker identification problem into a full text document retrieval-similar task. The prosody modeling labels the prosodic feature contours of a speaker's speech into sequences of prosody states. EPA then constructs a compact eigen-prosody space to represent the constellation of speakers. Furthermore, EPA is fused with a lower-level a priori knowledge interpolation (AKI) handset distortion compensator to complement each other. Experimental results on the HTIMIT database had shown that about 41.0% and 32.8% relative error rate reduction for seen and unseen handsets, respectively, was achieved comparing with the maximum a priori-adapted Gaussian mixture model/cepstral mean subtraction (MAP-GMM/CMS) baseline.

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