Knowledge-based formant tracking with confidence measure using dynamic programming

Abstract

In this study, we report on refinements to a formant tracking technique originally reported in [Xia et al., ICSLP (2000)]. The formant tracker operates in two phases. First, it finds optimal formant track estimates in oral sonorant regions by imposing frequency continuity constraints using dynamic programming. Second, post-processing is performed to make the estimates more robust and accurate, and to extend formant tracks in nasal and obstruent regions. In recent work, we have improved on our initial estimates of the formants by combining the outputs from a 12th-order LPC analysis and a 16th-order LPC analysis. Additionally, we have added a confidence measure for each formant track in each frame. The confidence measure is based on formant continuity, competing formants, short-time energy, context information, and formant information over the entire utterance. The experiments show that most of the tracking errors are associated with a low confidence value, while the correct formants have high confidence values. The performance of the algorithm in the sonorant regions was tested using randomly selected male and female speech from the TIMIT database. [Work supported by NSF Grant No. BCS0236707.]