1. A method of evaluating an audio message, comprising: storing a generic language model; storing a generic non-speech metafeatures model that produces a statistical likelihood that a particular non-speech metafeature will be associated with a spam message; extracting a non-speech metafeature associated with the audio message to yield an extracted non-speech metafeature; modifying the generic language model and generic non-speech metafeatures model based on actions of a user when managing spam messages, without a spam list, to generate a personal language model and a personal non-speech metafeatures model; identifying via a processor the audio message as the spam message or a non-spam message by comparing extracted language content of the audio message with the personal language model and comparing the extracted non-speech metafeature with the personal non-speech metafeatures model; and classifying the audio message as one of a spam message and a non-spam message based on a spam likelihood ratio generated by comparing the extracted language content of the audio message with a stored spam model and a stored non-spam model.