MUPET-Mouse Ultrasonic Profile ExTraction: A Signal Processing Tool for Rapid and Unsupervised Analysis of Ultrasonic Vocalizations.

Vocalizations play a significant role in social communication across species. Analyses in rodents have used a limited number of spectro-temporal measures to compare ultrasonic vocalizations (USVs), which limits the ability to address repertoire complexity in the context of behavioral states. Using an automated and unsupervised signal processing approach, we report the development of MUPET (Mouse Ultrasonic Profile ExTraction) software, an open-access MATLAB tool that provides data-driven, high-throughput analyses of USVs. MUPET measures, learns, and compares syllable types and provides an automated time stamp of syllable events. Using USV data from a large mouse genetic reference panel and open-source datasets produced in different social contexts, MUPET analyzes the fine details of syllable production and repertoire use. MUPET thus serves as a new tool for USV repertoire analyses, with the capability to be adapted for use with other species.

Directly data-derived articulatory gesture-like representations retain discriminatory information about phone categories.

How the speech production and perception systems evolved in humans still remains a mystery today. Previous research suggests that human auditory systems are able, and have possibly evolved, to preserve maximal information about the speaker's articulatory gestures. This paper attempts an initial step towards answering the complementary question of whether speakers' articulatory mechanisms have also evolved to produce sounds that can be optimally discriminated by the listener's auditory system. To this end we explicitly model, using computational methods, the extent to which derived representations of "primitive movements" of speech articulation can be used to discriminate between broad phone categories. We extract interpretable spatio-temporal primitive movements as recurring patterns in a data matrix of human speech articulation, i.e. representing the trajectories of vocal tract articulators over time. To this end, we propose a weakly-supervised learning method that attempts to find a part-based representation of the data in terms of recurring basis trajectory units (or primitives) and their corresponding activations over time. For each phone interval, we then derive a feature representation that captures the co-occurrences between the activations of the various bases over different time-lags. We show that this feature, derived entirely from activations of these primitive movements, is able to achieve a greater discrimination relative to using conventional features on an interval-based phone classification task. We discuss the implications of these findings in furthering our understanding of speech signal representations and the links between speech production and perception systems.