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).
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). 2. The method of claim 1 wherein the acoustic parameters include canonical syllables, and the acoustic parameters are divided into four categories, including a rhythm/ syllabicity category, a low spectral tilt and high pitch control category, a wide formant bandwidth and low pitch control category, and a duration of islands within utterances category.