Detecting user engagement in everyday conversations
This paper presents a novel application of speech emotion recognition: estimation of the level of conversational engagement between users of a voice communication system. We begin by using machine learning techniques, such as the support vector machine (SVM), to classify users’ emotions as expressed in individual utterances. However, this alone fails to model the temporal and interactive aspects of conversational engagement. We therefore propose the use of a multilevel structure based on coupled hidden Markov models (HMM) to estimate engagement levels in continuous natural speech. The first level is comprised of SVM-based classifiers that recognize emotional states, which could be (e.g.) discrete emotion types or arousal/valence levels. A high-level HMM then uses these emotional states as input, estimating users’ engagement in conversation by decoding the internal states of the HMM. We report experimental results obtained by applying our algorithms to the LDC EMOTIONAL PROSODY and CALLFRIEND speech corpora.

title: The emergence of links between lexical acquisition and object categorization: a computational study
abstract: Language is about symbols, and those symbols must be grounded in the physical world. Children learn to associate language with sensorimotor experiences during their development. In light of this, we first provide a computational account of how words are mapped to their perceptually grounded meanings. Moreover, the main part of this work proposes and implements a computational model of how word learning influences the formation of object categories to which those words refer. This model simulates the bi-directional relationship between word and object category learning: (1) object categorization provides mental representations of meanings that are mapped to words to form lexical items; (2) linguistic labels help object categorization by providing additional teaching signals; and (3) these two learning processes interplay with each other and form a developmental feedback loop. Compared with the method that performs these two tasks separately, our model shows promising improvements in both word-to-world mapping and perceptual categorization, suggesting a unified view of lexical and category learning in an integrative framework. Most importantly, this work provides a cognitively plausible explanation of the mechanistic nature of early word learning and object learning from co-occurring multisensory data.

title: A multimodal learning interface for grounding spoken language in sensory perceptions
abstract: Most speech interfaces are based on natural language processing techniques that use pre-defined symbolic representations of word meanings and process only linguistic information. To understand and use language like their human counterparts in multimodal human-computer interaction, computers need to acquire spoken language and map it to other sensory perceptions. This paper presents a multimodal interface that learns to associate spoken language with perceptual features by being situated in users' everyday environments and sharing user-centric multisensory information. The learning interface is trained in unsupervised mode in which users perform everyday tasks while providing natural language descriptions of their behaviors. We collect acoustic signals in concert with multisensory information from non-speech modalities, such as user's perspective video, gaze positions, head directions and hand movements. The system firstly estimates users' focus of attention from eye and head cues. Attention, as represented by gaze fixation, is used for spotting the target object of user interest. Attention switches are calculated and used to segment an action sequence into action units which are then categorized by mixture hidden Markov models. A multimodal learning algorithm is developed to spot words from continuous speech and then associate them with perceptually grounded meanings extracted from visual perception and action. Successful learning has been demonstrated in the experiments of three natural tasks: "unscrewing a jar", "stapling a letter" and "pouring water".

title: Attentional object spotting by integrating multimodal input
abstract: An intelligent human-computer interface is expected to allow computers to work with users in a cooperative manner. To achieve this goal, computers need to be aware of user attention and provide assistance without explicit user requests. Cognitive studies of eye movements suggest that in accomplishing well-learned tasks, the performer's focus of attention is locked onto ongoing work and more than 90% of eye movements are closely related to the objects being manipulated in the tasks. In light of this, we have developed an attentional object spotting system that integrates multimodal data consisting of eye position, head position and video from the "first-person" perspective. To detect the user's focus of attention, we modeled eye gaze and head movements using a hidden Markov model (HMM) representation. For each attentional point in time, the object of user interest is automatically extracted and recognized. We report the results of experiments on finding attentional objects in the natural task of "making a peanut-butter sandwich".

title: On the Integration of Grounding Language and Learning Objects
abstract: This paper presents a multimodal learning system that can ground spoken names of objects in their physical referents and learn to recognize those objects simultaneously from naturally co-occurring multisensory input. There are two technical problems involved: (1) the correspondence problem in symbol grounding - how to associate words (symbols) with their perceptually grounded meanings from multiple cooccurrences between words and objects in the physical environment. (2) object learning - how to recognize and categorize visual objects. We argue that those two problems can be fundamentally simplified by considering them in a general system and incorporating the spatio-temporal and cross-modal constraints of multimodal data. The system collects egocentric data including image sequences as well as speech while users perform natural tasks. It is able to automatically infer the meanings of object names from vision, and categorize objects based on teaching signals potentially encoded in speech. The experimental results reported in this paper reveal the effectiveness of using multimodal data and integrating heterogeneous techniques in machine learning, natural language processing and computer vision.

title: A multimodal learning interface for grounding spoken language in sensory perceptions
abstract: We present a multimodal interface that learns words from natural interactions with users. In light of studies of human language development, the learning system is trained in an unsupervised mode in which users perform everyday tasks while providing natural language descriptions of their behaviors. The system collects acoustic signals in concert with user-centric multisensory information from nonspeech modalities, such as user's perspective video, gaze positions, head directions, and hand movements. A multimodal learning algorithm uses this data to first spot words from continuous speech and then associate action verbs and object names with their perceptually grounded meanings. The central ideas are to make use of nonspeech contextual information to facilitate word spotting, and utilize body movements as deictic references to associate temporally cooccurring data from different modalities and build hypothesized lexical items. From those items, an EM-based method is developed to select correct word--meaning pairs. Successful learning is demonstrated in the experiments of three natural tasks: "unscrewing a jar," "stapling a letter," and "pouring water."

title: Querying Complex Structured Databases
abstract: Correctly generating a structured query (e.g., an XQuery or a SQL query) requires the user to have a full understanding of the database schema, which can be a daunting task. Alternative query models have been proposed to give users the ability to query the database without schema knowledge. Those models, including simple keyword search and labeled keyword search, aim to extract meaningful data fragments that match the structure-free query conditions (e.g., keywords) based on various matching semantics. Typically, the matching semantics are content-based: they are defined on data node inter-relationships and incur significant query evaluation cost. Our first contribution is a novel matching semantics based on analyzing the database schema. We show that query models employing a schema-based matching semantics can reduce query evaluation cost significantly while maintaining or even improving result quality. 
 
The adoption of schema-based matching semantics does not change the nature of those query models: they are still schema-ignorant, i.e., users express no schema knowledge (except the labels in labeled keyword search) in the query. While those models work well for some queries on some databases, they often encounter problems when applied to complex queries on databases with complex schemas. Our second contribution is a novel query model that incorporates partial schema knowledge through the use of schema summary. This new summary-aware query model, called Meaningful Summary Query (MSQ), seamlessly integrates summary-based structural conditions and structure-free conditions, and enables ordinary users to query complex databases. We design algorithms for evaluating MSQ queries, and demonstrate that MSQ queries can produce better results against complex databases when compared with previous approaches, and that they can be efficiently evaluated.

title: Schema-Free XQuery
abstract: The widespread adoption of XML holds out the promise that document structure can be exploited to specify precise database queries. However, the user may have only a limited knowledge of the XML structure, and hence may be unable to produce a correct XQuery, especially in the context of a heterogeneous information collection. The default is to use keyword-based search and we are all too familiar with how difficult it is to obtain precise answers by these means. We seek to address these problems by introducing the notion of Meaningful Lowest Common Ancestor Structure (MLCAS) for finding related nodes within an XML document. By automatically computing MLCAS and expanding ambiguous tag names, we add new functionality to XQuery and enable users to take full advantage of XQuery in querying XML data precisely and efficiently without requiring (perfect) knowledge of the document structure. Such a Schema-Free XQuery is potentially of value not just to casual users with partial knowledge of schema, but also to experts working in a data integration or data evolution context. In such a context, a schema-free query, once written, can be applied universally to multiple data sources that supply similar content under different schemas, and applied "forever" as these schemas evolve. Our experimental evaluation found that it was possible to express a wide variety of queries in a schema-free manner and have them return correct results over a broad diversity of schemas. Furthermore, the evaluation of a schema-free query is not expensive using a novel stack-based algorithm we develop for computing MLCAS: from 1 to 4 times the execution time of an equivalent schema-aware query.

title: An effective approach to document retrieval via utilizing WordNet and recognizing phrases
abstract: Noun phrases in queries are identified and classified into four types: proper names, dictionary phrases, simple phrases and complex phrases. A document has a phrase if all content words in the phrase are within a window of a certain size. The window sizes for different types of phrases are different and are determined using a decision tree. Phrases are more important than individual terms. Consequently, documents in response to a query are ranked with matching phrases given a higher priority. We utilize WordNet to disambiguate word senses of query terms. Whenever the sense of a query term is determined, its synonyms, hyponyms, words from its definition and its compound words are considered for possible additions to the query. Experimental results show that our approach yields between 23% and 31% improvements over the best-known results on the TREC 9, 10 and 12 collections for short (title only) queries, without using Web data.

title: Personalized Web search for improving retrieval effectiveness
abstract: Current Web search engines are built to serve all users, independent of the special needs of any individual user. Personalization of Web search is to carry out retrieval for each user incorporating his/her interests. We propose a novel technique to learn user profiles from users' search histories. The user profiles are then used to improve retrieval effectiveness in Web search. A user profile and a general profile are learned from the user's search history and a category hierarchy, respectively. These two profiles are combined to map a user query into a set of categories which represent the user's search intention and serve as a context to disambiguate the words in the user's query. Web search is conducted based on both the user query and the set of categories. Several profile learning and category mapping algorithms and a fusion algorithm are provided and evaluated. Experimental results indicate that our technique to personalize Web search is both effective and efficient.

title: Distributed Top-N Query Processing with Possibly Uncooperative Local Systems
abstract: We consider the problem of processing top-N queries in a distributed environment with possibly uncooperative local database systems. For a given top-N query, the problem is to find the N tuples that satisfy the query the best but not necessarily completely in an efficient manner. Top-N queries are gaining popularity in relational databases and are expected to be very useful for e-commerce applications. Many companies provide the same type of goods and services to the public on the Web, and relational databases may be employed to manage the data. It is not feasible for a user to query a large number of databases. It is therefore desirable to provide a facility where a user query is accepted at some site, suitable tuples from appropriate sites are retrieved and the results are merged and then presented to the user. In this paper, we present a method for constructing the desired facility. Our method consists of two steps. The first step determines which databases are likely to contain the desired tuples for a given query so that the databases can be ranked based on their desirability with respect to the query. Four different techniques are introduced for this step with one requiring no cooperation from local systems. The second step determines how the ranked databases should be searched and what tuples from the searched databases should be returned. A new algorithm is proposed for this purpose. Experimental results are presented to compare different methods and very promising results are obtained using the method that requires no cooperation from local databases.