A feature-based time domain pitch tracker
Michael Phillips · The Journal of the Acoustical Society of America · 1985
A pitch tracking algorithm was designed that uses perceptually motivated features to identify the first peak of each pitch period in the speech waveform during voiced portions of speech. The feature measurement algorithms were designed to capture all of the information that a person uses to identify pitch marks when looking at a waveform display. A multi-variate classifier makes decisions about the location of pitch marks based on the values of the feature measurements. This classifier was designed using Classgraph—a program that allows the user to examine hand-labeled data and make decision boundaries in the multidimensional feature space. Performance was evaluated by comparing pitch marks generated by the algorithm with hand-labeled pitch marks on a database of speakers each saying a different sentence. Each sentence was hand-labeled by two people. The agreement among labelers was within 1% of the agreement between each labeler and the output of the algorithm. [Supported by NSF and DARAP.]