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AUTOMATIC QUANTIFICATION AND PREDICTION
OF HUMAN SUBJECTIVE JUDGMENTS
IN BEHAVIORAL SIGNAL PROCESSING
by
Matthew P. Black
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Ful llment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(ELECTRICAL ENGINEERING)
May 2012
Copyright 2012 Matthew P. Black
Object Description
| Title | Automatic quantification and prediction of human subjective judgments in behavioral signal processing |
| Author | Black, Matthew P. |
| Author email | mattpblack@gmail.com;mattpblack@gmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Electrical Engineering |
| School | Viterbi School of Engineering |
| Date defended/completed | 2012-03-20 |
| Date submitted | 2012-03-20 |
| Date approved | 2012-03-21 |
| Restricted until | 2012-03-21 |
| Date published | 2012-03-21 |
| Advisor (committee chair) | Narayanan, Shrikanth S. |
| Advisor (committee member) |
Ortega, Antonio Margolin, Gayla |
| Abstract | Human judgments on human behavior are an important part of interpersonal interactions and many assessment and intervention designs. While humans have evolved to be naturally adept at processing behavioral information, there are some challenges. Namely, human descriptions on behaviors are oftentimes qualitative, and there is variability between people's judgments due to the subjective nature of the judgment process. ❧ Technology can help humans process behavioral data in a number of ways. Quantitative descriptors can be extracted from objective signals (e.g., audio, video) that represent aspects of human behavior in consistent and repeatable ways. There are many emerging engineering pursuits centered around modeling human behavior. Much of this research focuses on modeling specific human actions (e.g., head nods) during acted or non-spontaneous scenarios. Behavioral signal processing involves the development of computational methods that model human behavior in real-life scenarios. In this thesis, we automatically quantify and predict human subjective judgments on human behavior from speech signals in the context of societally-significant domain applications (education, family studies, health), where human observers play a critical role. ❧ There are many technological challenges to quantifying and predicting human subjective judgments on human behavior. These include modeling several sources of variability, including the human behavior itself (heterogeneity) and the human evaluators themselves. There is a need to extract robust generalizable features that capture the human behavior and the relevant perceptual cues human evaluators are using. In addition, there is possibly information across multiple modalities/cues, and it is not always clear how humans weight them when making their judgments. Many relevant human judgments are ""gist-like"" based off a large amount of behavioral data. Thus, modeling the data at possibly multiple granularities is important, since some temporal regions may be more relevant than others and a particular cue's importance may vary as a function of time. Finally, since we are analyzing real data in real-life scenarios, the human behavior can be complex and the data can be non-ideal (e.g., noisy). ❧ For this thesis, we focused on concrete problem domains that highlighted specific aspects of the technological challenges: literacy assessment, couples therapy research, and autism diagnosis. In the literacy assessment domain, we show that we can exploit human-inspired information into the computational framework for accurate modeling of evaluator's perception of children's overall reading ability for one specific reading task. We fused features that represented multiples aspects of the human behavior and robustly emulated human observational subjective processes by learning from individual and multiple evaluators' judgments. We also exploit the fact that evaluators' level of agreement significantly varies (depending on the child being judged) by incorporating this source of evaluator variability in the modeling framework. In the couples therapy research, we analyze a large corpus of spontaneous dyadic interactions between married couples and show we can predict six relevant high-level observational judgments (e.g., level of acceptance, global negative affect) using speaker-dependent acoustic speech features. Furthermore, we demonstrate one method for fusing automatically-derived speech and language information for improved classification of spouses' level of blame (high vs. low). Finally, we discuss our effort in collecting a multimodal corpus of child-psychologist interactions, recorded in the context of a social interaction used by psychologists for a research-level diagnosis of autism spectrum disorders. We highlight initial work with this corpus and discuss future experiments for the quantification of psychologists' clinical judgments on atypical social behavior (e.g., atypical prosody). ❧ This thesis is on the development of a quantitative, automated framework that emulates human observational processes to describe human behavior from speech signals. We hope it makes impactful technological contributions to modeling complex human subjective processes. This work represents a significant step towards a shift in engineering from modeling and recognizing more objective human behaviors (e.g., speech recognition) to quantifying more subtle and abstract ones, a central theme to the emerging area of behavioral signal processing. |
| Keyword | behavioral signal processing (BSP); human-centered engineering; speech and language processing |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m |
| Rights | Black, Matthew P. |
| Access conditions | The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given. |
| Repository name | University of Southern California Digital Library |
| Repository address | USC Digital Library, University of Southern California, University Park Campus MC 7002, 106 University Village, Los Angeles, California 90089-7002, USA |
| Repository email | cisadmin@usc.edu |
| Archival file | uscthesesreloadpub_Volume71/etd-BlackMatth-534.pdf |
Description
| Title | Page 1 |
| Full text | AUTOMATIC QUANTIFICATION AND PREDICTION OF HUMAN SUBJECTIVE JUDGMENTS IN BEHAVIORAL SIGNAL PROCESSING by Matthew P. Black A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Ful llment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (ELECTRICAL ENGINEERING) May 2012 Copyright 2012 Matthew P. Black |
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