15. A method for detecting autism, comprising: transforming an audio recording to output an indication of autism for a key child on an output mechanism selected from the group consisting of a display, a printing device, an electronic storage device, and an audio output device; the transforming of the audio recording performed by comparing the audio recording to a model developed by analyzing transparent parameters of a plurality of sound recordings captured in a natural language environment, wherein the transparent parameters are developed through clustering the transparent parameters from recordings of known children, the clustering the transparent parameters is performed according to an unsupervised clustering method, the unsupervised clustering method comprising K-means clustering, the transparent parameters of known children are tuned to autism, and the analyzing the transparent parameters for each sound recording of the plurality of sound recordings comprises: segmenting the sound recording into a plurality of recording segments, wherein the sound recording is captured by a microphone and sound recorder combination; determining which of the plurality of recording segments correspond to the key child; determining which of the plurality of recording segments that correspond to the key child are classified as key child recordings; and extracting acoustic parameters of the key child recordings.
15. A method for detecting autism, comprising: transforming an audio recording to output an indication of autism for a key child on an output mechanism selected from the group consisting of a display, a printing device, an electronic storage device, and an audio output device; the transforming of the audio recording performed by comparing the audio recording to a model developed by analyzing transparent parameters of a plurality of sound recordings captured in a natural language environment, wherein the transparent parameters are developed through clustering the transparent parameters from recordings of known children, the clustering the transparent parameters is performed according to an unsupervised clustering method, the unsupervised clustering method comprising K-means clustering, the transparent parameters of known children are tuned to autism, and the analyzing the transparent parameters for each sound recording of the plurality of sound recordings comprises: segmenting the sound recording into a plurality of recording segments, wherein the sound recording is captured by a microphone and sound recorder combination; determining which of the plurality of recording segments correspond to the key child; determining which of the plurality of recording segments that correspond to the key child are classified as key child recordings; and extracting acoustic parameters of the key child recordings. 17. The method of claim 15 , wherein: the key child recordings are broken down into cry, vegetative-sound, and vocalization categories; and the extracting the acoustic parameters of the key child recordings is applied only to the vocalization category of the key child recordings.