An HMM-Like Dynamic Time Warping Scheme for Automatic Speech Recognition

In the past, the kernel of automatic speech recognition (ASR) is dynamic time warping (DTW), which is feature-based template matching and belongs to the category technique of dynamic programming (DP). Although DTW is an early developed ASR technique, DTW has been popular in lots of applications. DTW is playing an important role for the known Kinect-based gesture recognition application now. This paper proposed an intelligent speech recognition system using an improved DTW approach for multimedia and home automation services. The improved DTW presented in this work, called HMM-like DTW, is essentially a hidden Markov model- (HMM-) like method where the concept of the typical HMM statistical model is brought into the design of DTW. The developed HMM-like DTW method, transforming feature-based DTW recognition into model-based DTW recognition, will be able to behave as the HMM recognition technique and therefore proposed HMM-like DTW with the HMM-like recognition model will have the capability to further perform model adaptation (also known as speaker adaptation). A series of experimental results in home automation-based multimedia access service environments demonstrated the superiority and effectiveness of the developed smart speech recognition system by HMM-like DTW

Does it match? Analyzing self-reported online dermatology match data to Charting Outcomes in the Match

Dermatology is arguably the most competitive residency (81.6% match rate for United States allopathic seniors) with significantly more applicants than available positions. To objectify this process, the National Residency Match Program (NRMP) has produced bi-annual Charting Outcomes in Match (COM) datasets, which aggregate data from the prior two application cycles and tabulate statistics to aid applicants. In parallel, online forums provide medical trainees with vast amounts of information, including residency application insights. Reddit medical school subforum compiles annual spreadsheets of anonymous, individualized applicant data to aid future applicants. We compared this data to NRMP data to show that although the data means are similar (e.g. Step 1), the Reddit dermatology spreadsheet collects more data and the individualized nature aids applicants in a personalized way unlike the mean aggregate data in NRMP. Under univariate analysis, Alpha Omega Alpha status, overall publications, and dermatology-specific publications are associated with interview invitation rates. Although limitations of the study include small data size and reporting bias, this is the first of its kind to our knowledge to compare these two often-used tools to aid dermatology applicants. Future endeavors should expand anonymous data reporting and use the data to carry out more extensive studies to investigate factors influencing the application process

Incorporation of uncertainties in real-time catchment flood forecasting

Floods have become the most prevalent and costly natural hazards in the U.S. When preparing real-time flood forecasts for a catchment flood warning and preparedness system, consideration must be given to four sources of uncertainty -- natural, data, model parameters, and model structure. A general procedure has been developed for applying reliability analysis to evaluate the effects of the various sources of uncertainty on hydrologic models used for forecasting and prediction of catchment floods. Three reliability analysis methods -- Monte Carlo simulation, mean value and advanced first-order second moment analyses (MVFOSM and AFOSM, respectively) - - were applied to the rainfall -runoff modeling reliability problem. Comparison of these methods indicates that the AFOSM method is probably best suited to the rainfall-runoff modeling reliability problem with the MVFOSM showing some promise. The feasibility and utility of the reliability analysis procedure are shown for a case study employing as an example the HEC-1 and RORB rainfall-runoff watershed models to forecast flood events on the Vermilion River watershed at Pontiac, Illinois. The utility of the reliability analysis approach is demonstrated for four important hydrologic problems: 1) determination of forecast (or prediction) reliability, 2) determination of the flood level exceedance probability due to a current storm and development of "rules of thumb" for flood warning decision making considering this probabilistic information, 3) determination of the key sources of uncertainty influencing model forecast reliability, 4) selection of hydrologic models based on comparison of model forecast reliability. Central to this demonstration is the reliability analysis methods' ability to estimate the exceedance probability for any hydrologic target level of interest and, hence, to produce forecast cumulative density functions and probability distribution functions. For typical hydrologic modeling cases, reduction of the underlying modeling uncertainties is the key to obtaining useful, reliable forecasts. Furthermore, determination of the rainfall excess is the primary source of uncertainty, especially in the estimation of the temporal and areal rainfall distributions.U.S. Department of the InteriorU.S. Geological SurveyOpe

Advanced methodology for storm sewer design—phase II

This report describes further development of computer models for determining the diameter, slope and elevations of each pipe in a storm drainage system in which the layout and manhole locations are specified. The design procedure is based on a least-cost criterion and utilizes discrete differential dynamic programming as the search technique. In this phase of the study a detention storage capability has been added to the model using two approaches. The first approach requires the specification of a maximum allowable outflow and computes the required storage. The second approach determines the storage volume such that the sum of the storage and pipe system costs is a minimum. The procedure for computation of expected damage costs has been changed to reflect the variation of flood damage with flood volume. Also a surface runoff component has been added. This option uses the hydrograph generation portion of the Illinois Urbana Drainage Area Simulator model. Improved cost specification methods as well as flexible pipe elevation constraint capabilities have been added. The new developments are illustrated using two example basins.U.S. Department of the InteriorU.S. Geological SurveyOpe

Developments of Machine Learning Schemes for Dynamic Time-Wrapping-Based Speech Recognition

This paper presents a machine learning scheme for dynamic time-wrapping-based (DTW) speech recognition. Two categories of learning strategies, supervised and unsupervised, were developed for DTW. Two supervised learning methods, incremental learning and priority-rejection learning, were proposed in this study. The incremental learning method is conceptually simple but still suffers from a large database of keywords for matching the testing template. The priority-rejection learning method can effectively reduce the matching time with a slight decrease in recognition accuracy. Regarding the unsupervised learning category, an automatic learning approach, called "most-matching learning, " which is based on priority-rejection learning, was developed in this study. Most-matching learning can be used to intelligently choose the appropriate utterances for system learning. The effectiveness and efficiency of all three proposed machine-learning approaches for DTW were demonstrated using keyword speech recognition experiments

Optical music recognition of the singer using formant frequency estimation of vocal fold vibration and lip motion with interpolated GMM classifiers

The main work of this paper is to identify the musical genres of the singer by performing the optical detection of lip motion. Recently, optical music recognition has attracted much attention. Optical music recognition in this study is a type of automatic techniques in information engineering, which can be used to determine the musical style of the singer. This paper proposes a method for optical music recognition where acoustic formant analysis of both vocal fold vibration and lip motion are employed with interpolated Gaussian mixture model (GMM) estimation to perform musical genre classification of the singer. The developed approach for such classification application is called GMM-Formant. Since humming and voiced speech sounds cause periodic vibrations of the vocal folds and then the corresponding motion of the lip, the proposed GMM-Formant firstly operates to acquire the required formant information. Formant information is important acoustic feature data for recognition classification. The proposed GMM-Formant method then uses linear interpolation for combining GMM likelihood estimates and formant evaluation results appropriately. GMM-Formant will effectively adjust the estimated formant feature evaluation outcomes by referring to certain degree of the likelihood score derived from GMM calculations. The superiority and effectiveness of presented GMM-Formant are demonstrated by a series of experiments on musical genre classification of the singer

Advanced methodologies for design of storm sewer systems

This report describes the development of a series of computer models capable of determining the diameter, slope and crown elevations of each sewer in a storm drainage system in which the layout and manhole locations are predetermined. The criterion for design decisions is the generation of a least-cost system. The basis for all of the models is the application of discrete differential dynamic programing (DDDP) as the optimization tool. Two important concepts are introduced as optimal model components: hydrograph routing and risks and uncertainties in designs. Three routing procedures are adopted, each with its own advantages. Expected flood damage costs are evaluated through the analysis of numerous risks and uncertainties associated with the design. This analysis permits the estimation of the probability of exceeding the capacity and the corresponding expected assessed damage of any sewer in the system. The expected damage cost is added to the installation cost to obtain the total cost which is then minimized in the DDDP procedure. Two example sewer systems are used as a basis for illustrating different aspects of the various least-cost design models and developing user guidelines.U.S. Department of the InteriorU.S. Geological SurveyOpe