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

Kawasaki Disease Shock Syndrome vs Classical Kawasaki Disease: A Meta-analysis and Comparison With SARS-CoV-2 Multisystem Inflammatory Syndrome.

BACKGROUND: The emergence of increasing reports worldwide of a severe inflammatory process and shock in pediatric patients resembling Kawasaki disease (KD) and more specifically Kawasaki disease shock syndrome (KDSS), prompted us to explore KDSS in a preamble of a systematic comparison between the two conditions. METHODS: We completed a systematic review of KDSS and performed a meta-analysis comparison between reported KDSS cases and KD controls. RESULTS: A total of ten case-control series were included in the meta-analysis. KDSS patients were older (38.4 ± 30.6 vs. 21.9±19.5 months; P<0.001) compared to standard KD with equal sex distribution and completeness of clinical diagnostic criteria. KDSS present higher CRP (59.4±29.2 mg/dL vs. 20.8±14.8 mg/dL; p<0.001), lower albumin (2.7±0.5 g/dL vs. 3.3±0.5 g/dL; p<0.01), and lower platelets (255±149 109/L vs. 394±132 109/L; p<0.001) but only borderline higher WBC's (p=0.06). Differences in ALT, AST and ESR were non-significant. The odds of IVIG resistance (44.4% vs. 9.6%; (p<0.001) and the hospital length of stay (10.9±5.8 vs. 5.0±3.0 days; p<0.001) were higher in KDSS as were the odds of coronary artery abnormalities (33.9% vs. 8.6%; p<0.001). CONCLUSION: This first meta-analysis on KDSS versus KD represents a basis for future works on KDSS and opens the opportunity for future multicenter studies in the search of causal relationships between presenting elements and the eventual complications of KDSS. The similarities between SARS-CoV-2 multisystem inflammatory syndrome in children (MIS-C) and KDSS open new horizons to the understanding of the etiology and pathophysiology related to KDSS

Medium-Term Complications Associated With Coronary Artery Aneurysms After Kawasaki Disease: A Study From the International Kawasaki Disease Registry.

Background Coronary artery aneurysms (CAAs) may occur after Kawasaki disease (KD) and lead to important morbidity and mortality. As CAA in patients with KD are rare and heterogeneous lesions, prognostication and risk stratification are difficult. We sought to derive the cumulative risk and associated factors for cardiovascular complications in patients with CAAs after KD. Methods and Results A 34-institution international registry of 1651 patients with KD who had CAAs (maximum CA

The development and assessment of biological treatments for children

The development of biological agents with specific immunological targets has revolutionized the treatment of a wide variety of paediatric diseases where traditional immunosuppressive agents have been partly ineffective or intolerable. The increasing requirement for pharmaceutical companies to undertake paediatric studies has provided impetus for studies of biologics in children. The assessment of biological agents in children to date has largely relied upon randomized controlled trials using a withdrawal design, rather than a parallel study design. This approach has been largely used due to ethical concerns, including use of placebo treatments in children with active chronic disease, and justified on the basis that treatments have usually already undergone robust assessment in related adult conditions. However, this study design limits the reliability of the data and can confuse the interpretation of safety results. Careful ongoing monitoring of safety and efficacy in real-world practice through national and international biologics registries and robust reporting systems is crucial. The most commonly used biological agents in children target tumour necrosis factor-α, interleukin-1, interleukin-6 and cytotoxic lymphocyte-associated antigen-4. These agents are most frequently used in paediatric rheumatic diseases. This review discusses the development and assessment of biologics within paediatric rheumatology with reference to the lessons learned from use in other subspecialties

Kawasaki syndrome: an intriguing disease with numerous unsolved dilemmas

More than 40 years have passed since Kawasaki syndrome (KS) was first described. Yet KS still remains an enigmatic illness which damages the coronary arteries in a quarter of untreated patients and is the most common cause of childhood-acquired heart disease in developed countries. Many gaps exist in our knowledge of the etiology and pathogenesis of KS, making improvements in therapy difficult. In addition, many KS features and issues still demand further efforts to achieve a much better understanding of the disease. Some of these problem areas include coronary artery injuries in children not fulfilling the classic diagnostic criteria, genetic predisposition to KS, unpredictable ineffectiveness of current therapy in some cases, vascular dysfunction in patients not showing echocardiographic evidence of coronary artery abnormalities in the acute phase of KS, and risk of potential premature atherosclerosis. Also, the lack of specific laboratory tests for early identification of the atypical and incomplete cases, especially in infants, is one of the main obstacles to beginning treatment early and thereby decreasing the incidence of cardiovascular involvement. Transthoracic echocardiography remains the gold-standard for evaluation of coronary arteries in the acute phase and follow-up. In KS patients with severe vascular complications, more costly and potentially invasive investigations such as coronary CT angiography and MRI may be necessary. As children with KS with or without heart involvement become adolescents and adults, the recognition and treatment of the potential long term sequelae become crucial, requiring that rheumatologists, infectious disease specialists, and cardiologists cooperate to develop specific guidelines for a proper evaluation and management of these patients. More education is needed for physicians and other professionals about how to recognize the long-term impact of systemic problems related to KS

An empirical study of automatic accent classification

This paper extends language identification (LID) techniques to a large scale accent classification task: 23-way classifica-tion of foreign-accented English. We find that a purely acous-tic approach based on a combination of heteroscedastic linear discriminant analysis (HLDA) and maximum mutual infor-mation (MMI) training is very effective. In contrast to LID tasks, methods based on parallel languagemodels provemuch less effective. We focus on the Oregon Graduate Institute Foreign-Accented English dataset, and obtain a detection rate of 32%, which to our knowledge is the best reported result for 23-way accent classification