1. A method for tone recognition in spoken language processing, the method comprising: with a processor module, a) determining an auditory spectrum for an input window of sound; b) extracting one or more multi-scale features from the auditory spectrum, wherein each multi-scale feature is extracted using a separate two-dimensional spectro-temporal receptive filter; c) generating one or more feature maps corresponding to the one or more multi-scale features; d) extracting an auditory gist vector from each of the one or more feature maps; e) obtaining a cumulative gist vector through augmentation of each auditory gist vector extracted from the one or more feature maps; f) determining one or more tonal characteristics corresponding to the input window of sound by mapping the cumulative gist vector to one or more tonal characteristics using a machine learning algorithm; g) identifying a sequence of phonemes in the input window of sound, wherein the sequence of phonemes matches a plurality of different lexical meanings in a tone language; h) combining the identified sequence of phonemes with the determined tonal characteristics; and i) identifying a lexical meaning in the tone language of the combination from among the plurality of different lexical meanings by determining a match between the combination and a corresponding tone and phoneme sequence combination in a speech recognition database for the tone language.
1. A method for tone recognition in spoken language processing, the method comprising: with a processor module, a) determining an auditory spectrum for an input window of sound; b) extracting one or more multi-scale features from the auditory spectrum, wherein each multi-scale feature is extracted using a separate two-dimensional spectro-temporal receptive filter; c) generating one or more feature maps corresponding to the one or more multi-scale features; d) extracting an auditory gist vector from each of the one or more feature maps; e) obtaining a cumulative gist vector through augmentation of each auditory gist vector extracted from the one or more feature maps; f) determining one or more tonal characteristics corresponding to the input window of sound by mapping the cumulative gist vector to one or more tonal characteristics using a machine learning algorithm; g) identifying a sequence of phonemes in the input window of sound, wherein the sequence of phonemes matches a plurality of different lexical meanings in a tone language; h) combining the identified sequence of phonemes with the determined tonal characteristics; and i) identifying a lexical meaning in the tone language of the combination from among the plurality of different lexical meanings by determining a match between the combination and a corresponding tone and phoneme sequence combination in a speech recognition database for the tone language. 7. The method of claim 1 , wherein one of the multi-scale features in b) characterizes intensities associated with the auditory spectrum.