1. A method for detecting autism in a natural language environment using a microphone, a sound recorder, and a computer programmed with software for a specialized purpose of processing recordings captured by a combination of the microphone and the sound recorder, the computer programmed to execute the method comprising: segmenting an audio signal captured by the combination using the computer programmed for the specialized purpose into a plurality of recording segments; determining which of the plurality of recording segments correspond to a key child; determining which of the plurality of recording segments that correspond to the key child are classified as key child recordings; extracting acoustic parameters of the key child recordings; comparing the acoustic parameters of the key child recordings to known acoustic parameters for children, wherein the known acoustic parameters are developed through clustering the known acoustic parameters from recordings of known children using K-means clustering, and the known acoustic parameters of known children are tuned to autism; and determining a likelihood of autism for the key child.
1. A method for detecting autism in a natural language environment using a microphone, a sound recorder, and a computer programmed with software for a specialized purpose of processing recordings captured by a combination of the microphone and the sound recorder, the computer programmed to execute the method comprising: segmenting an audio signal captured by the combination using the computer programmed for the specialized purpose into a plurality of recording segments; determining which of the plurality of recording segments correspond to a key child; determining which of the plurality of recording segments that correspond to the key child are classified as key child recordings; extracting acoustic parameters of the key child recordings; comparing the acoustic parameters of the key child recordings to known acoustic parameters for children, wherein the known acoustic parameters are developed through clustering the known acoustic parameters from recordings of known children using K-means clustering, and the known acoustic parameters of known children are tuned to autism; and determining a likelihood of autism for the key child. 10. The method of claim 1 , further comprising: performing acoustic analysis on the key child recordings.