Automated formant tracking using reassigned spectrograms

Abstract

The measurement and tracking of formant frequencies is usually accomplished with a linear prediction spectrum estimate, combined with a heuristic-based tracking algorithm. Despite the broad acceptance of this procedure over decades, there is a known bias towards the nearest harmonic, and formant estimates are less accurate for speech with higher fundamental frequency. Previous research has established the superior accuracy of formant measurement using the reassigned spectrogram; however, the measurements must be done by hand, severely limiting its usefulness. Here we present a technique that can locate formants automatically in a reassigned spectrogram. First, the reassigned spectrogram of the signal must be heavily “pruned” so that only a few points remain which highlight the detected frequency components, which are the presumed formants. Second, we apply a simple ridge-finding routine to the pruned spectrogram, to determine the formant tracks automatically. This process was tested on speech samples—/hVd/-words—by four speakers previously used in a comparison of 5 automatic algorithms and manually-measured reassigned spectrograms, and on breathy and falling tones in a speaker of Hmong. The results of this process are generally as good or better than the manual measurement when the optimal parameters for ridge-finding are used.