Developing attribute acquisition strategies in spoken dialogue systems via user simulation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 159-169).A spoken dialogue system (SDS) is an application that supports conversational interaction with a human to perform some task. SDSs are emerging as an intuitive and efficient means for accessing information. A critical barrier to their widespread deployment remains in the form of communication breakdown at strategic points in the dialogue, often when the user tries to supply a named entity from a large or open vocabulary set. For example, a weather system might know several thousand cities, but there is no easy way to inform the user about what those cities are. The system will likely misrecognize any unknown city as some known city. The inability of a system to acquire an unknown value can lead to unpredictable behavior by the system, as well as by the user. This thesis presents a framework for developing attribute acquisition strategies with a simulated user. We specifically focus on the acquisition of unknown city names in a flight domain, through a spell-mode subdialogue. Collecting data from real users is costly in both time and resources. In addition, our goal is to focus on situations that tend to occur sporadically in real dialogues, depending on the domain and the user's experience in that domain.(cont.) Therefore, we chose to employ user simulation, which would allow us to generate a large number of dialogues, and to configure the input as desired in order to exercise specific strategies. We present a novel method of utterance generation for user simulation, that exploits an existing corpus of real user dialogues, but recombines the utterances using an example-based, template approach. Items of interest not in the corpus, such as foreign or unknown cities, can be included by splicing in synthesized speech. This method allows us to produce realistic utterances by retaining the structural variety of real user utterances, while introducing cities that can only be resolved via spelling. We also developed a model of generic dialogue management, allowing a developer to quickly specify interaction properties on a per-attribute basis. This model was used to assess the effectiveness of various combinations of dialogue strategies and simulated user behavior. Current approaches to user simulation typically model simulated utterances at the intention level, assuming perfect recognition and understanding. We employ speech to develop our strategies in the context of errors that occur naturally from recognition and understanding.(cont.) We use simulation to address two problems: the conflict problem requires the system to choose how to act when a new hypothesis for an attribute conflicts with its current belief, while the compliance problem requires the system to decide whether a user was compliant with a spelling request. Decision models were learned from simulated data, and were tested with real users, showing that the learned model significantly outperformed a heuristic model in choosing the "ideal" response to the conflict problem, with accuracies of 84.1% and 52.1%, respectively. The learned model to predict compliance achieved a respectable 96.3% accuracy. These results suggest that such models learned from simulated data can attain similar, if not better, performance in dialogues with real users.by Edward A. Filisko.Ph.D

Doctor of Philosophy

dissertationBiomedical data are a rich source of information and knowledge. Not only are they useful for direct patient care, but they may also offer answers to important population-based questions. Creating an environment where advanced analytics can be performed against biomedical data is nontrivial, however. Biomedical data are currently scattered across multiple systems with heterogeneous data, and integrating these data is a bigger task than humans can realistically do by hand; therefore, automatic biomedical data integration is highly desirable but has never been fully achieved. This dissertation introduces new algorithms that were devised to support automatic and semiautomatic integration of heterogeneous biomedical data. The new algorithms incorporate both data mining and biomedical informatics techniques to create "concept bags" that are used to compute similarity between data elements in the same way that "word bags" are compared in data mining. Concept bags are composed of controlled medical vocabulary concept codes that are extracted from text using named-entity recognition software. To test the new algorithm, three biomedical text similarity use cases were examined: automatically aligning data elements between heterogeneous data sets, determining degrees of similarity between medical terms using a published benchmark, and determining similarity between ICU discharge summaries. The method is highly configurable and 5 different versions were tested. The concept bag method performed particularly well aligning data elements and outperformed the compared algorithms by iv more than 5%. Another configuration that included hierarchical semantics performed particularly well at matching medical terms, meeting or exceeding 30 of 31 other published results using the same benchmark. Results for the third scenario of computing ICU discharge summary similarity were less successful. Correlations between multiple methods were low, including between terminologists. The concept bag algorithms performed consistently and comparatively well and appear to be viable options for multiple scenarios. New applications of the method and ideas for improving the algorithm are being discussed for future work, including several performance enhancements, configuration-based enhancements, and concept vector weighting using the TF-IDF formulas

Examining First-Graders' Construction of Knowledge of Graphophonemic and Orthographic Relationships: Reading and Writing Student-Selected Continuous Text

The purpose of this study was to examine first-graders' construction of knowledge of graphophonemic and orthographic relationships. Three levels of treatment were assigned randomly to three groups of first-graders in their first semester of first grade. Treatment varied in student engagement with reading and writing texts based on student interests and in the amount of interaction students had with one another and the researcher as they read, wrote, and examined words, word patterns, and graphophonemic relationships. The study was based on a quasi-experimental nonequivalent control group design (Campbell & Stanley, 1963) with an added within-subjects factor of 12 weekly test occasions. These weekly tests involved students writing a researcher-dictated continuous text selected by students in the full-treatment group from the larger portion of text read each week. Additional elements of qualitative research were included in the design and analyses. Quantitative analyses revealed statistically significant results. Qualitative data analyses confirmed that students who interacted daily with each other and the researcher in reading and writing activities constructed more knowledge about graphophonemic and orthographic relationships than peers from the partial-treatment group and the control group. Results led to conclusions and implications involving a reexamination of current and traditional methods of spelling instruction and assessment for young children

Towards multi-domain speech understanding with flexible and dynamic vocabulary

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.Includes bibliographical references (p. 201-208).In developing telephone-based conversational systems, we foresee future systems capable of supporting multiple domains and flexible vocabulary. Users can pursue several topics of interest within a single telephone call, and the system is able to switch transparently among domains within a single dialog. This system is able to detect the presence of any out-of-vocabulary (OOV) words, and automatically hypothesizes each of their pronunciation, spelling and meaning. These can be confirmed with the user and the new words are subsequently incorporated into the recognizer lexicon for future use. This thesis will describe our work towards realizing such a vision, using a multi-stage architecture. Our work is focused on organizing the application of linguistic constraints in order to accommodate multiple domain topics and dynamic vocabulary at the spoken input. The philosophy is to exclusively apply below word-level linguistic knowledge at the initial stage. Such knowledge is domain-independent and general to all of the English language. Hence, this is broad enough to support any unknown words that may appear at the input, as well as input from several topic domains. At the same time, the initial pass narrows the search space for the next stage, where domain-specific knowledge that resides at the word-level or above is applied. In the second stage, we envision several parallel recognizers, each with higher order language models tailored specifically to its domain. A final decision algorithm selects a final hypothesis from the set of parallel recognizers.(cont.) Part of our contribution is the development of a novel first stage which attempts to maximize linguistic constraints, using only below word-level information. The goals are to prevent sequences of unknown words from being pruned away prematurely while maintaining performance on in-vocabulary items, as well as reducing the search space for later stages. Our solution coordinates the application of various subword level knowledge sources. The recognizer lexicon is implemented with an inventory of linguistically motivated units called morphs, which are syllables augmented with spelling and word position. This first stage is designed to output a phonetic network so that we are not committed to the initial hypotheses. This adds robustness, as later stages can propose words directly from phones. To maximize performance on the first stage, much of our focus has centered on the integration of a set of hierarchical sublexical models into this first pass. To do this, we utilize the ANGIE framework which supports a trainable context-free grammar, and is designed to acquire subword-level and phonological information statistically. Its models can generalize knowledge about word structure, learned from in-vocabulary data, to previously unseen words. We explore methods for collapsing the ANGIE models into a finite-state transducer (FST) representation which enables these complex models to be efficiently integrated into recognition. The ANGIE-FST needs to encapsulate the hierarchical knowledge of ANGIE and replicate ANGIE's ability to support previously unobserved phonetic sequences ...by Grace Chung.Ph.D

A Study of Experiences Provided in the Home Environment Associated With Accelerated Reading Abilities as Reported by Parents of Intellectually Superior Preschoolers.

The purpose of this study was to look beyond the general effect that intelligence and environment have upon the development of reading and investigate (a) differences in how accelerated readers and nonreaders perform on written language tasks and (b) differences in written language experiences provided in the home environment, as reported by parents, associated with the development of accelerated reading abilities. Due to the broadness of the term written language, only four aspects of written language were examined (i.e., awareness of concepts about print, awareness of print in the environment, awareness of letter/sound correspondences in words--invented spelling, and awareness during story reading episodes--story recall). Thirty 4- and 5-year-old intellectually superior children who resided in supportive home environments participated in the study. The sample was comprised of 15 accelerated readers and 15 nonreaders. All subjects were administered four tasks which measured each of the written language areas. In addition, mothers of all subjects were administered a parent questionnaire comprised of 24 descriptive and 245 quantitative questions which examined experiences in the home environment associated with the four written language areas. Results indicated that accelerated readers performed significantly better than nonreaders on two (i.e., print in the environment and invented spelling) of the four tasks than nonreaders. Significant differences were also found in two areas on the parent questionnaire. Mothers of accelerated readers reported providing more opportunities in the home environment related to concepts about print and story recall than mothers of nonreaders. To further examine associations that existed between significant differences in performance on the tasks and significant differences in opportunities reported in the questionnaire constructs, related analysis was conducted. Results indicated that opportunities provided in the home environment related to story recall and concepts about print could be associated with the ability of accelerated readers to read print in the environment and engage in invented spelling tasks

Linguistically-motivated sub-word modeling with applications to speech recognition

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Includes bibliographical references (p. 173-185).Despite the proliferation of speech-enabled applications and devices, speech-driven human-machine interaction still faces several challenges. One of theses issues is the new word or the out-of-vocabulary (OOV) problem, which occurs when the underlying automatic speech recognizer (ASR) encounters a word it does not "know". With ASR being deployed in constantly evolving domains such as restaurant ratings, or music querying, as well as on handheld devices, the new word problem continues to arise.This thesis is concerned with the OOV problem, and in particular with the process of modeling and learning the lexical properties of an OOV word through a linguistically-motivated sub-syllabic model. The linguistic model is designed using a context-free grammar which describes the sub-syllabic structure of English words, and encapsulates phonotactic and phonological constraints. The context-free grammar is supported by a probability model, which captures the statistics of the parses generated by the grammar and encodes spatio-temporal context. The two main outcomes of the grammar design are: (1) sub-word units, which encode pronunciation information, and can be viewed as clusters of phonemes; and (2) a high-quality alignment between graphemic and sub-word units, which results in hybrid entities denoted as spellnemes. The spellneme units are used in the design of a statistical bi-directional letter-to-sound (L2S) model, which plays a significant role in automatically learning the spelling and pronunciation of a new word.The sub-word units and the L2S model are assessed on the task of automatic lexicon generation. In a first set of experiments, knowledge of the spelling of the lexicon is assumed. It is shown that the phonemic pronunciations associated with the lexicon can be successfully learned using the L2S model as well as a sub-word recognizer.(cont.) In a second set of experiments, the assumption of perfect spelling knowledge is relaxed, and an iterative and unsupervised algorithm, denoted as Turbo-style, makes use of spoken instances of both spellings and words to learn the lexical entries in a dictionary.Sub-word speech recognition is also embedded in a parallel fashion as a backoff mechanism for a word recognizer. The resulting hybrid model is evaluated in a lexical access application, whereby a word recognizer first attempts to recognize an isolated word. Upon failure of the word recognizer, the sub-word recognizer is manually triggered. Preliminary results show that such a hybrid set-up outperforms a large-vocabulary recognizer.Finally, the sub-word units are embedded in a flat hybrid OOV model for continuous ASR. The hybrid ASR is deployed as a front-end to a song retrieval application, which is queried via spoken lyrics. Vocabulary compression and open-ended query recognition are achieved by designing a hybrid ASR. The performance of the frontend recognition system is reported in terms of sentence, word, and sub-word error rates. The hybrid ASR is shown to outperform a word-only system over a range of out-of-vocabulary rates (1%-50%). The retrieval performance is thoroughly assessed as a fmnction of ASR N-best size, language model order, and the index size. Moreover, it is shown that the sub-words outperform alternative linguistically-motivated sub-lexical units such as phonemes. Finally, it is observed that a dramatic vocabulary compression - by more than a factor of 10 - is accompanied by a minor loss in song retrieval performance.by Ghinwa F. Choueiter.Ph.D

Strategies for Handling Out-of-Vocabulary Words in Automatic Speech Recognition

Nowadays, most ASR (automatic speech recognition) systems deployed in industry are closed-vocabulary systems, meaning we have a limited vocabulary of words the system can recognize, and where pronunciations are provided to the system. Words out of this vocabulary are called out-of-vocabulary (OOV) words, for which either pronunciations or both spellings and pronunciations are not known to the system. The basic motivations of developing strategies to handle OOV words are: First, in the training phase, missing or wrong pronunciations of words in training data results in poor acoustic models. Second, in the test phase, words out of the vocabulary cannot be recognized at all, and mis-recognition of OOV words may affect recognition performance of its in-vocabulary neighbors as well. Therefore, this dissertation is dedicated to exploring strategies of handling OOV words in closed-vocabulary ASR. First, we investigate dealing with OOV words in ASR training data, by introducing an acoustic-data driven pronunciation learning framework using a likelihood-reduction based criterion for selecting pronunciation candidates from multiple sources, i.e. standard grapheme-to-phoneme algorithms (G2P) and phonetic decoding, in a greedy fashion. This framework effectively expands a small hand-crafted pronunciation lexicon to cover OOV words, for which the learned pronunciations have higher quality than approaches using G2P alone or using other baseline pruning criteria. Furthermore, applying the proposed framework to generate alternative pronunciations for in-vocabulary (IV) words improves both recognition performance on relevant words and overall acoustic model performance. Second, we investigate dealing with OOV words in ASR test data, i.e. OOV detection and recovery. We first conduct a comparative study of a hybrid lexical model (HLM) approach for OOV detection, and several baseline approaches, with the conclusion that the HLM approach outperforms others in both OOV detection and first pass OOV recovery performance. Next, we introduce a grammar-decoding framework for efficient second pass OOV recovery, showing that with properly designed schemes of estimating OOV unigram probabilities, the framework significantly improves OOV recovery and overall decoding performance compared to first pass decoding. Finally we propose an open-vocabulary word-level recurrent neural network language model (RNNLM) re-scoring framework, making it possible to re-score lattices containing recovered OOVs using a single word-level RNNLM, that was ignorant of OOVs when it was trained. Above all, the whole OOV recovery pipeline shows the potential of a highly efficient open-vocabulary word-level ASR decoding framework, tightly integrated into a standard WFST decoding pipeline

Effects of Rhyming Instruction on Learning the Alphabetic Principle, Phonemic Awareness, and Rhyming Complexity Skills with At-Risk Prekindergarten Students

At-risk prekindergarten students (i.e., low SES, speech-language impaired) typically lag behind their peers in phonological awareness and other emergent literacy skills such as letter knowledge and vocabulary (Duursma et al., 2008; Lundberg, 2009). However, there is a limited amount of research that has studied the efficacy of phonological interventions for at-risk children (Ziolkowski & Goldstein, 2008). Because of the long-lived debate concerning the role of rhyme versus the role of phoneme awareness, it is uncertain whether learning rhyming skills will provide the most facilitative context to learn other emergent literacy skills (e.g., letter knowledge, phonemic awareness). The current study investigated the effects of an 8-week intervention on learning the alphabetic principle (i.e., letter knowledge, phonemic awareness), phonemic awareness skills, and rhyming complexity skills. The existence of a continuum of rhyming complexity skills (e.g., expressive rhyming, rhyming couplets) was also explored. Twenty-nine at-risk prekindergarten students received an intervention focused on rhyme awareness (i.e., rhyme) or an intervention focused on phoneme awareness. The results of the study revealed both groups made statistically comparable progress on letter knowledge, phonemic awareness, word reading, and rhyming complexity skills. A visual inspection of gains scores and cut-off scores for weekly probes revealed differential progress by the type of intervention received. Participants with a suspected or diagnosed speech-language impairment were not significantly different from their peers at the conclusion of the study. The result of the study also indicated that rhyming skills exist on continuum of complexity with reciting nursery rhymes being the least complex and coordinating sound and rhyme being the most complex