1. A method for extracting a prosodic feature of a speech signal, comprising: dividing the speech signal into speech frames; transforming the speech frames from time domain to frequency domain; calculating respective prosodic features for different frequency ranges; extracting a traditional acoustics feature for each speech frame; calculating, for each said prosodic feature, a feature associated with a current frame, a difference between the feature associated with the current frame and a feature associated with a previous frame, and a difference between the feature associated with the current frame and an average of respective features in a speech segment of the current frame; extracting a fundamental frequency of the current frame, a difference between the fundamental frequency of the current frame and a fundamental frequency of the previous frame, and a difference between the fundamental frequency of the current frame and an average of respective fundamental frequencies in the speech segment of the current frame; and recognizing speech associated with the speech signal based on said calculating for each said prosodic feature and said extracting the fundamental frequency, wherein said calculating the respective prosodic features for different frequency ranges includes one or more of the following: calculating a thickness feature of the speech signal for a first frequency range, wherein the thickness feature is based on frequency domain energy of the first frequency range; calculating a strength feature of the speech signal for a second frequency range, wherein the strength feature is based on time domain energy of the second frequency range; and calculating a contour feature of the speech signal for a third frequency range, wherein the contour feature is based on a time domain envelope of the third frequency range.
1. A method for extracting a prosodic feature of a speech signal, comprising: dividing the speech signal into speech frames; transforming the speech frames from time domain to frequency domain; calculating respective prosodic features for different frequency ranges; extracting a traditional acoustics feature for each speech frame; calculating, for each said prosodic feature, a feature associated with a current frame, a difference between the feature associated with the current frame and a feature associated with a previous frame, and a difference between the feature associated with the current frame and an average of respective features in a speech segment of the current frame; extracting a fundamental frequency of the current frame, a difference between the fundamental frequency of the current frame and a fundamental frequency of the previous frame, and a difference between the fundamental frequency of the current frame and an average of respective fundamental frequencies in the speech segment of the current frame; and recognizing speech associated with the speech signal based on said calculating for each said prosodic feature and said extracting the fundamental frequency, wherein said calculating the respective prosodic features for different frequency ranges includes one or more of the following: calculating a thickness feature of the speech signal for a first frequency range, wherein the thickness feature is based on frequency domain energy of the first frequency range; calculating a strength feature of the speech signal for a second frequency range, wherein the strength feature is based on time domain energy of the second frequency range; and calculating a contour feature of the speech signal for a third frequency range, wherein the contour feature is based on a time domain envelope of the third frequency range. 7. The method according to claim 1 , wherein said calculating the respective prosodic features for different frequency ranges is performed based on each frame.