Early and Late Stage Mechanisms for Vocalization Processing in the Human Auditory System

The human auditory system is able to rapidly process incoming acoustic information, actively filtering, categorizing, or suppressing different elements of the incoming acoustic stream. Vocalizations produced by other humans (conspecifics) likely represent the most ethologically-relevant sounds encountered by hearing individuals. Subtle acoustic characteristics of these vocalizations aid in determining the identity, emotional state, health, intent, etc. of the producer. The ability to assess vocalizations is likely subserved by a specialized network of structures and functional connections that are optimized for this stimulus class. Early elements of this network would show sensitivity to the most basic acoustic features of these sounds; later elements may show categorically-selective response patterns that represent high-level semantic organization of different classes of vocalizations. A combination of functional magnetic resonance imaging and electrophysiological studies were performed to investigate and describe some of the earlier and later stage mechanisms of conspecific vocalization processing in human auditory cortices. Using fMRI, cortical representations of harmonic signal content were found along the middle superior temporal gyri between primary auditory cortices along Heschl\u27s gyri and the superior temporal sulci, higher-order auditory regions. Additionally, electrophysiological findings also demonstrated a parametric response profile to harmonic signal content. Utilizing a novel class of vocalizations, human-mimicked versions of animal vocalizations, we demonstrated the presence of a left-lateralized cortical vocalization processing hierarchy to conspecific vocalizations, contrary to previous findings describing similar bilateral networks. This hierarchy originated near primary auditory cortices and was further supported by auditory evoked potential data that suggests differential temporal processing dynamics of conspecific human vocalizations versus those produced by other species. Taken together, these results suggest that there are auditory cortical networks that are highly optimized for processing utterances produced by the human vocal tract. Understanding the function and structure of these networks will be critical for advancing the development of novel communicative therapies and the design of future assistive hearing devices

A Computational Model of Auditory Feature Extraction and Sound Classification

This thesis introduces a computer model that incorporates responses similar to those found in the cochlea, in sub-corticai auditory processing, and in auditory cortex. The principle aim of this work is to show that this can form the basis for a biologically plausible mechanism of auditory stimulus classification. We will show that this classification is robust to stimulus variation and time compression. In addition, the response of the system is shown to support multiple, concurrent, behaviourally relevant classifications of natural stimuli (speech). The model incorporates transient enhancement, an ensemble of spectro - temporal filters, and a simple measure analogous to the idea of visual salience to produce a quasi-static description of the stimulus suitable either for classification with an analogue artificial neural network or, using appropriate rate coding, a classifier based on artificial spiking neurons. We also show that the spectotemporal ensemble can be derived from a limited class of 'formative' stimuli, consistent with a developmental interpretation of ensemble formation. In addition, ensembles chosen on information theoretic grounds consist of filters with relatively simple geometries, which is consistent with reports of responses in mammalian thalamus and auditory cortex. A powerful feature of this approach is that the ensemble response, from which salient auditory events are identified, amounts to stimulus-ensemble driven method of segmentation which respects the envelope of the stimulus, and leads to a quasi-static representation of auditory events which is suitable for spike rate coding. We also present evidence that the encoded auditory events may form the basis of a representation-of-similarity, or second order isomorphism, which implies a representational space that respects similarity relationships between stimuli including novel stimuli

Multimodal Affect Recognition: Current Approaches and Challenges

Many factors render multimodal affect recognition approaches appealing. First, humans employ a multimodal approach in emotion recognition. It is only fitting that machines, which attempt to reproduce elements of the human emotional intelligence, employ the same approach. Second, the combination of multiple-affective signals not only provides a richer collection of data but also helps alleviate the effects of uncertainty in the raw signals. Lastly, they potentially afford us the flexibility to classify emotions even when one or more source signals are not possible to retrieve. However, the multimodal approach presents challenges pertaining to the fusion of individual signals, dimensionality of the feature space, and incompatibility of collected signals in terms of time resolution and format. In this chapter, we explore the aforementioned challenges while presenting the latest scholarship on the topic. Hence, we first discuss the various modalities used in affect classification. Second, we explore the fusion of modalities. Third, we present publicly accessible multimodal datasets designed to expedite work on the topic by eliminating the laborious task of dataset collection. Fourth, we analyze representative works on the topic. Finally, we summarize the current challenges in the field and provide ideas for future research directions