Divide-And-Warp Temporal Alignment Of Speech Signals Between Speakers: Validation Using Articulatory Data

Meaningful comparisons between sets of speech-induced, dynamically evolving articulatory measurements require that the data be temporally aligned in a manner invariant to speech rate discrepancies. The best known approach to this problem is to apply dynamic time warping (DTW) to the corresponding audio signals. While the usefulness of DTW methods is well established in automatic speech recognition, they were never directly and quantitatively validated as a way of aligning and comparing signals from different speakers in a way that is useful for the study of speech as a biological process. This paper provides the first direct quantitative validation of such an audio-based temporal alignment algorithm, itself based on a new divide-and-warp strategy, using speaker invariant temporal landmarks from articulatory data. Results demonstrate that the proposed temporal alignment algorithm accurately brings these landmarks into correspondence between speakers (mean absolute delay of similar to 35ms).