A multistage algorithm for fricative spotting

We present an algorithm for spotting fricative consonants in continuous speech. Fricative spotting can be useful in professional audio applications, where excessive accentuation of these phonemes can degrade the aesthetics of voice recordings, or in applications for the hearing-impaired, where certain manipulations can increase their perception. All stages of our algorithm rely only on features extracted directly from the audio signal and on common classification techniques, making it simple to implement and language-invariant. In the first stage, a linear classifier, pre-trained using the Fisher´s Linear Discriminant Analysis (LDA) method, is used to detect fricatives inside speech sentences. In the second stage, the detected phonemes are further analyzed using a decision-tree classifier, attempting to reject false detections. Tested on the full corpus of the TIMIT audio database the algorithm achieved very good detection rates across the entire range of fricative phonemes.