A human speech detection method detects pure-speech signals in an audio signal containing a mixture of pure-speech and non-speech or mixed-speech signals. The method accurately detects the pure-speech signals by computing a novel Valley Percentage feature from the audio signal and then classifying the audio signals into pure-speech and non-speech (or mixed-speech) classifications. The Valley Percentage is a measurement of the low energy parts of the audio signal (the valley) in comparison to the high energy parts of the audio signal (the mountain). To classify the audio signal, the method performs a threshold decision on the value of the Valley Percentage. Using a binary mask, a high Valley Percentage is classified as pure-speech and a low Valley Percentage is classified as non-speech (or mixed-speech). The method further employs morphological filters to improve the accuracy of human speech detection. Before detection, a morphological closing filter may be employed to eliminate unwanted noise from the audio signal. After detection, a combination of morphological closing and opening filters may be employed to remove aberrant pure-speech and non-speech classifications from the binary mask resulting from impulsive audio signals in order to more accurately detect the boundaries between the pure-speech and non-speech portions of the audio signal. A number of parameters may be employed by the method to further improve the accuracy of human speech detection. For implementation in supervised digital audio signal applications, these parameters may be optimized by training the application a priori. For implementation in an unsupervised environment, adaptive determination of these parameters is also possible.