A method for training a neural network of a neural network based speaker classifier for use in speaker change detection. The method comprises: a) preprocessing input speech data; b) extracting a plurality of feature frames from the preprocessed input speech data; c) normalizing the extracted feature frames of each speaker within the preprocessed input speech data with each speaker's mean and variance; d) concatenating the normalized feature frames to form overlapped longer frames having a frame length and a hop size; e) inputting the overlapped longer frames to the neural network based speaker classifier; and f) training the neural network through forward-backward propagation.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for training a neural network of a neural network based speaker classifier for use in speaker change detection, the method comprising: a) preprocessing input speech data; b) extracting a plurality of feature frames from the preprocessed input speech data; c) normalizing the extracted feature frames of each speaker within the preprocessed input speech data with each speaker's mean and variance; d) concatenating the normalized feature frames to form overlapped longer frames having a frame length and a hop size; e) inputting the overlapped longer frames to the neural network based speaker classifier; and f) training the neural network through forward-backward propagation. 2. The method of claim 1, wherein step (a) comprises: a.1) scaling a maximum of absolute amplitude of the input speech data to one; a.2) performing voice activity detection on the scaled input speech data; and a.3) eliminating unvoiced portions of the scaled input speech data. 3. The method of claim 2, wherein step (a.2) comprises: a.2.1) segmenting the scaled input speech data into overlapped frames with a window size and a hop size; a.2.2) determine a short-term energy E of each frame; a.2.3) determine a spectral centroid C of each frame; a.2.4) remove any frame from the scaled input speech data in which the short-term energy E of the frame is below a predetermined threshold TE; and a.2.5) remove any frame from the scaled input speech data in which the spectral centroid C of the frame is below a predetermined threshold TC. 4. The method of claim 3, wherein the window size is 50 ms and the hop size is 25 ms. 5. The method of claim 3, wherein the short-term energy E comprises: E = 1 N  ∑ n = 1 N    s  ( n )  2 , where s(n) is the frame data with N samples. 6. The method of claim 3, wherein the spectral centroid C comprises: C = ∑ k = 1 K  kS  ( k ) ∑ k = 1 K  S  ( k ) . wherein S(k) is a Discrete Fourier Transform (DFT) of frame data s(n) with k frequency components. 7. The method of claim 3, wherein: the predetermined threshold TE is a weighted average of the local maxima in a distribution histogram of the short-term energy E; and the predetermined threshold TC is a weighted average of the local maxima in a distribution histogram of the spectral centroid C. 8. The method of claim 1, wherein step (b) comprises generating 39-dimensional Mel-Frequency Cepstral Coefficients (MFCCs) from the preprocessed input speech data using overlapping Hamming windows and a hop size. 9. The method of claim 8, wherein the Hamming window is 25 ms and the hop size is 10 ms. 10. The method of claim 1, wherein the overlapped longer frame length is 10 frames and the hop size is 3 frames.