Communication error detection using facial expressions

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 129-135).Automatic detection of communication errors in conversational systems typically rely only on acoustic cues. However, perceptual studies have indicated that speakers do exhibit visual communication error cues passively during the system's conversational turn. In this thesis, we introduce novel algorithms for face and body gesture recognition and present the first automatic system for detecting communication errors using facial expressions during the system's turn. This is useful as it detects communication problems before the user speaks a reply. To detect communication problems accurately and efficiently we develop novel extensions to hidden-state discriminative methods. We also present results that show when human subjects become aware that the conversational system is capable of receiving visual input, they become more communicative visually yet naturally.by Sy Bor Wang.Ph.D

Crowd-supervised training of spoken language systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 155-166).Spoken language systems are often deployed with static speech recognizers. Only rarely are parameters in the underlying language, lexical, or acoustic models updated on-the-fly. In the few instances where parameters are learned in an online fashion, developers traditionally resort to unsupervised training techniques, which are known to be inferior to their supervised counterparts. These realities make the development of spoken language interfaces a difficult and somewhat ad-hoc engineering task, since models for each new domain must be built from scratch or adapted from a previous domain. This thesis explores an alternative approach that makes use of human computation to provide crowd-supervised training for spoken language systems. We explore human-in-the-loop algorithms that leverage the collective intelligence of crowds of non-expert individuals to provide valuable training data at a very low cost for actively deployed spoken language systems. We also show that in some domains the crowd can be incentivized to provide training data for free, as a byproduct of interacting with the system itself. Through the automation of crowdsourcing tasks, we construct and demonstrate organic spoken language systems that grow and improve without the aid of an expert. Techniques that rely on collecting data remotely from non-expert users, however, are subject to the problem of noise. This noise can sometimes be heard in audio collected from poor microphones or muddled acoustic environments. Alternatively, noise can take the form of corrupt data from a worker trying to game the system - for example, a paid worker tasked with transcribing audio may leave transcripts blank in hopes of receiving a speedy payment. We develop strategies to mitigate the effects of noise in crowd-collected data and analyze their efficacy. This research spans a number of different application domains of widely-deployed spoken language interfaces, but maintains the common thread of improving the speech recognizer's underlying models with crowd-supervised training algorithms. We experiment with three central components of a speech recognizer: the language model, the lexicon, and the acoustic model. For each component, we demonstrate the utility of a crowd-supervised training framework. For the language model and lexicon, we explicitly show that this framework can be used hands-free, in two organic spoken language systems.by Ian C. McGraw.Ph.D

Toward Widely-Available and Usable Multimodal Conversational Interfaces

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 159-166).Multimodal conversational interfaces, which allow humans to interact with a computer using a combination of spoken natural language and a graphical interface, offer the potential to transform the manner by which humans communicate with computers. While researchers have developed myriad such interfaces, none have made the transition out of the laboratory and into the hands of a significant number of users. This thesis makes progress toward overcoming two intertwined barriers preventing more widespread adoption: availability and usability. Toward addressing the problem of availability, this thesis introduces a new platform for building multimodal interfaces that makes it easy to deploy them to users via the World Wide Web. One consequence of this work is City Browser, the first multimodal conversational interface made publicly available to anyone with a web browser and a microphone. City Browser serves as a proof-of-concept that significant amounts of usage data can be collected in this way, allowing a glimpse of how users interact with such interfaces outside of a laboratory environment. City Browser, in turn, has served as the primary platform for deploying and evaluating three new strategies aimed at improving usability. The most pressing usability challenge for conversational interfaces is their limited ability to accurately transcribe and understand spoken natural language. The three strategies developed in this thesis - context-sensitive language modeling, response confidence scoring, and user behavior shaping - each attack the problem from a different angle, but they are linked in that each critically integrates information from the conversational context.by Alexander Gruenstein.Ph.D

Applications of Discourse Structure for Spoken Dialogue Systems

Language exhibits structure beyond the sentence level (e.g. the syntactic structure of a sentence). In particular, dialogues, either human-human or human-computer, have an inherent structure called the discourse structure. Models of discourse structure attempt to explain why a sequence of random utterances combines to form a dialogue or no dialogue at all. Due to the relatively simple structure of the dialogues that occur in the information-access domains of typical spoken dialogue systems (e.g. travel planning), discourse structure has often seen limited application in such systems. In this research, we investigate the utility of discourse structure for spoken dialogue systems in more complex domains, e.g. tutoring. This work was driven by two intuitions.First, we believed that the "position in the dialogue" is a critical information source for two tasks: performance analysis and characterization of dialogue phenomena. We define this concept using transitions in the discourse structure. For performance analysis, these transitions are used to create a number of novel factors which we show to be predictive of system performance. One of these factors informs a promising modification of our system which is implemented and compared with the original version of the system through a user study. Results show that the modification leads to objective improvements. For characterization of dialogue phenomena, we find statistical dependencies between discourse structure transitions and two dialogue phenomena which allow us to speculate where and why these dialogue phenomena occur and to better understand system behavior.Second, we believed that users will benefit from direct access to discourse structure information. We enable this through a graphical representation of discourse structure called the Navigation Map. We demonstrate the subjective and objective utility of the Navigation Map through two user studies.Overall, our work demonstrates that discourse structure is an important information source for designers of spoken dialogue systems