Automated Speech Discrimination using Frequency Derivative Threshold Detection

Traditionally the discrimination of speech from non-speech signals within audio has been done through the use of amplitude threshold levels or energy levels within the audio signals. While these methods along with other newer methods have been shown to provide effective results in discriminating speech, this paper aims at taking a different perspective in speech discrimination. In general the variations in frequency of a person´s voice over small time periods are within a limited range. In contrast many undesirable background noises tend to have highly varying frequency levels. Hence by analysing the change in frequency over time an effective means can be found for identifying what is human speech and what is noise. This paper aims at using this form of speech discrimination in order to develop an efficient method for isolating speech from non-speech signals.