A novel and robust parameter training approach for HMMs under noisy and partial access to states

Cataloged from PDF version of article.This paper proposes a new estimation algorithm for the parameters of an HMM as to best account for the observed data. In this model, in addition to the observation sequence, we have partial and noisy access to the hidden state sequence as side information. This access can be seen as "partial labeling" of the hidden states. Furthermore, we model possible mislabeling in the side information in a joint framework and derive the corresponding EM updates accordingly. In our simulations, we observe that using this side information, we considerably improve the state recognition performance, up to 70%, with respect to the "achievable margin" defined by the baseline algorithms. Moreover, our algorithm is shown to be robust to the training conditions. (C) 2013 Elsevier B.V. All rights reserved

Likelihood-Maximizing-Based Multiband Spectral Subtraction for Robust Speech Recognition

Automatic speech recognition performance degrades significantly when speech is affected by environmental noise. Nowadays, the major challenge is to achieve good robustness in adverse noisy conditions so that automatic speech recognizers can be used in real situations. Spectral subtraction (SS) is a well-known and effective approach; it was originally designed for improving the quality of speech signal judged by human listeners. SS techniques usually improve the quality and intelligibility of speech signal while speech recognition systems need compensation techniques to reduce mismatch between noisy speech features and clean trained acoustic model. Nevertheless, correlation can be expected between speech quality improvement and the increase in recognition accuracy. This paper proposes a novel approach for solving this problem by considering SS and the speech recognizer not as two independent entities cascaded together, but rather as two interconnected components of a single system, sharing the common goal of improved speech recognition accuracy. This will incorporate important information of the statistical models of the recognition engine as a feedback for tuning SS parameters. By using this architecture, we overcome the drawbacks of previously proposed methods and achieve better recognition accuracy. Experimental evaluations show that the proposed method can achieve significant improvement of recognition rates across a wide range of signal to noise ratios

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

Improving Reinforcement Learning Techniques for Medical Decision Making

Reinforcement learning (RL) is a powerful tool for developing personalized treatment regimens from healthcare data. In RL, an agent samples experiences from an environment (such as a model of patient health) to learn a policy that maximizes long-term reward. This dissertation proposes methodological and practical developments in the application of RL to treatment planning problems. First, we develop a novel time series model for simulating patient health states from observed clinical data. We use a generative neural network architecture that learns a direct mapping between distributions over clinical measurements at adjacent time points. We show that this model produces realistic patient trajectories and can be paired with on-policy RL to learn effective treatment policies. Second, we develop a novel extension of hidden Markov models, which are commonly used to model and predict patient health states. Specifically, we develop a special case of recurrent neural networks with the same likelihood function as a corresponding discrete-observation hidden Markov model. We demonstrate how combining our model with other predictive neural networks improves disease forecasting and offers novel clinical interpretations compared with a standard hidden Markov model. Third, we develop a method for selecting high-performing reinforcement learning-based treatment policies for underrepresented patient subpopulations using limited observations. Our method learns a probability distribution over treatment policies from a reference patient group, then adapts its recommendations using limited data from an underrepresented patient group. We show that our method outperforms state-of-the-art benchmarks in selecting effective treatment policies for patients with non-typical clinical characteristics, and predicting these patients\u27 outcomes under its policies. Finally, we use RL to optimize medication regimens for Parkinson\u27s disease patients using high-frequency wearable sensor data. We build an environment model of how patients\u27 symptoms respond to medication, then use RL to recommend optimal medication types, timing, and dosages for each patient. We show that these patient-specific RL-prescribed medication regimens outperform physician-prescribed regimens and provide clinically defensible treatment strategies. Our framework also enables physicians to identify patients who could could switch to lower-frequency regimens for improved adherence, and to identify patients who may be candidates for advanced therapies

Conflict and Computation on Wikipedia: a Finite-State Machine Analysis of Editor Interactions

What is the boundary between a vigorous argument and a breakdown of relations? What drives a group of individuals across it? Taking Wikipedia as a test case, we use a hidden Markov model to approximate the computational structure and social grammar of more than a decade of cooperation and conflict among its editors. Across a wide range of pages, we discover a bursty war/peace structure where the systems can become trapped, sometimes for months, in a computational subspace associated with significantly higher levels of conflict-tracking "revert" actions. Distinct patterns of behavior characterize the lower-conflict subspace, including tit-for-tat reversion. While a fraction of the transitions between these subspaces are associated with top-down actions taken by administrators, the effects are weak. Surprisingly, we find no statistical signal that transitions are associated with the appearance of particularly anti-social users, and only weak association with significant news events outside the system. These findings are consistent with transitions being driven by decentralized processes with no clear locus of control. Models of belief revision in the presence of a common resource for information-sharing predict the existence of two distinct phases: a disordered high-conflict phase, and a frozen phase with spontaneously-broken symmetry. The bistability we observe empirically may be a consequence of editor turn-over, which drives the system to a critical point between them.Comment: 23 pages, 3 figures. Matches published version. Code for HMM fitting available at http://bit.ly/sfihmm ; time series and derived finite state machines at bit.ly/wiki_hm