16. A method of assessing a key child's expressive language development, comprising: processing an audio recording using a computing device configured to determine a plurality of key child audio segments that correspond to the key child's vocalizations, wherein the processing includes segmenting and assigning a segment ID indicating a source to a first plurality of audio segments derived from the audio recording, the segmenting and assigning performed using a Minimum Duration Gaussian Mixture Model (MD-GMM), wherein the MD-GMM includes a plurality of models used for matching to the first plurality of audio segments, the plurality of models including a noise model that includes characteristics of sound attributable to noise, a key child model that includes characteristics of sounds from a hypothetical key child, and an adult model that includes characteristics of sound from an adult, wherein the segmenting and assigning assigns a portion of the first plurality of audio segments to the plurality of key child audio segments; receiving the plurality of key child audio segments that correspond to the key child's vocalizations; determining a distribution for each of a plurality of phone categories for the key child audio segments, wherein each of the phone categories corresponds to a pre-defined speech sound; selecting an age-based model, wherein the selected age-based model corresponds to the key child's chronological age and the age-based model includes a weight associated with each of the phone categories; and using the distribution in the selected age-based model to assess the key child's language development.
16. A method of assessing a key child's expressive language development, comprising: processing an audio recording using a computing device configured to determine a plurality of key child audio segments that correspond to the key child's vocalizations, wherein the processing includes segmenting and assigning a segment ID indicating a source to a first plurality of audio segments derived from the audio recording, the segmenting and assigning performed using a Minimum Duration Gaussian Mixture Model (MD-GMM), wherein the MD-GMM includes a plurality of models used for matching to the first plurality of audio segments, the plurality of models including a noise model that includes characteristics of sound attributable to noise, a key child model that includes characteristics of sounds from a hypothetical key child, and an adult model that includes characteristics of sound from an adult, wherein the segmenting and assigning assigns a portion of the first plurality of audio segments to the plurality of key child audio segments; receiving the plurality of key child audio segments that correspond to the key child's vocalizations; determining a distribution for each of a plurality of phone categories for the key child audio segments, wherein each of the phone categories corresponds to a pre-defined speech sound; selecting an age-based model, wherein the selected age-based model corresponds to the key child's chronological age and the age-based model includes a weight associated with each of the phone categories; and using the distribution in the selected age-based model to assess the key child's language development. 23. The method of claim 16 , wherein determining a distribution for each of a plurality of phone categories for the key child audio segments comprises applying an adult automatic speech recognition phone decoder to the audio segments.