Title: Unsupervised Latent Behavior Manifold Learning from Acoustic Features: audio2behavior,
Abstract: Behavioral annotation using signal processing and machine learning is highly
dependent on training data and manual annotations of behavioral labels.
Previous studies have shown that speech information encodes significant
behavioral information and be used in a variety of automated behavior
recognition tasks. However, extracting behavior information from speech is
still a difficult task due to the sparseness of training data coupled with the
complex, high-dimensionality of speech, and the complex and multiple
information streams it encodes. In this work we exploit the slow varying
properties of human behavior. We hypothesize that nearby segments of speech
share the same behavioral context and hence share a similar underlying
representation in a latent space. Specifically, we propose a Deep Neural
Network (DNN) model to connect behavioral context and derive the behavioral
manifold in an unsupervised manner. We evaluate the proposed manifold in the
couples therapy domain and also provide examples from publicly available data
(e.g. stand-up comedy). We further investigate training within the couples'
therapy domain and from movie data. The results are extremely encouraging and
promise improved behavioral quantification in an unsupervised manner and
warrants further investigation in a range of applications.