6. The method of claim 1 wherein the key child recordings are broken down into cry, vegetative sound, and vocalization categories; and the extracting of (d) is applied only to the vocalization category of the key child recordings.
1. A method for detecting autism in a natural language environment using a microphone, sound recorder, and a computer programmed with software for the specialized purpose of processing recordings captured by the microphone and sound recorder combination, the computer programmed to execute a method comprising: (a) segmenting an audio signal captured by the microphone and sound recorder combination using the computer programmed for the specialized purpose into a plurality of recording segments; (b) determining which of the plurality of recording segments correspond to a key child; (c) determining which of the plurality of recording segments that correspond to the key child are classified as key child recordings; (d) extracting phone-based features of the key child recordings; (e) comparing the phone-based features of the key child recordings to known phone-based features for children, the phone-based features corresponding to predetermined clusters of child speech resembling phones wherein the predetermined clusters of child speech resembling phones are clustered according to an unsupervised clustering method; (f) determining a likelihood of autism based on the comparing of (e); (g) extracting acoustic parameters of the key child recordings; and (h) comparing the acoustic parameters of the key child recordings to known acoustic parameters for children, wherein the determining of (f) also is based on the comparing of (h).