An abnormal frame detection method, comprising: obtaining a signal frame from a speech signal; dividing the signal frame into at least two subframes; obtaining a local energy value of a subframe of the signal frame; obtaining , according to the local energy value of the subframe, a first characteristic value used to indicate a local energy trend of the signal frame, wherein, the local energy trend indicates a change in the energy of the signal frame ; performing singularity analysis on the signal frame to obtain a second characteristic value used to indicate a singularity characteristic of the signal frame; and determining the signal frame as an abnormal frame if the first characteristic value of the signal frame meets a first threshold and the second characteristic value of the signal frame meets a second threshold; wherein the performing singularity analysis on the signal frame to obtain a second characteristic value used to indicate a singularity characteristic comprises: performing wavelet decomposition on the signal frame to obtain a wavelet coefficient, and, performing signal reconstruction according to the wavelet coefficient to obtain a reconstructed signal frame W(n); and obtaining the second characteristic value E2 by using the formula: E 2 = max log W 2 n − average log W 2 n ; wherein max(log(W 2 (n))) and average(log(W 2 (n))) are a maximum value and an average value of W 2 (n) in the logarithm domain respectively. obtaining a signal frame from a speech signal; dividing the signal frame into at least two subframes; obtaining a local energy value of a subframe of the signal frame; obtaining , according to the local energy value of the subframe, a first characteristic value used to indicate a local energy trend of the signal frame, wherein, the local energy trend indicates a change in the energy of the signal frame ; performing singularity analysis on the signal frame to obtain a second characteristic value used to indicate a singularity characteristic of the signal frame; and determining the signal frame as an abnormal frame if the first characteristic value of the signal frame meets a first threshold and the second characteristic value of the signal frame meets a second threshold; wherein the performing singularity analysis on the signal frame to obtain a second characteristic value used to indicate a singularity characteristic comprises: performing wavelet decomposition on the signal frame to obtain a wavelet coefficient, and, performing signal reconstruction according to the wavelet coefficient to obtain a reconstructed signal frame W(n); and obtaining the second characteristic value E2 by using the formula: E 2 = max log W 2 n − average log W 2 n ; wherein max(log(W 2 (n))) and average(log(W 2 (n))) are a maximum value and an average value of W 2 (n) in the logarithm domain respectively. performing wavelet decomposition on the signal frame to obtain a wavelet coefficient, and, performing signal reconstruction according to the wavelet coefficient to obtain a reconstructed signal frame W(n); and obtaining the second characteristic value E2 by using the formula: E 2 = max log W 2 n − average log W 2 n ; wherein max(log(W 2 (n))) and average(log(W 2 (n))) are a maximum value and an average value of W 2 (n) in the logarithm domain respectively. <EOS>