Improving ultrasound-based multimodal speech recognition with predictive features from representation learning.

Representation learning is believed to produce high-level representations of underlying dynamics in temporal sequences. A three-dimensional convolutional neural network trained to predict future frames in ultrasound tongue and optical lip images creates features for a continuous hidden Markov model based speech recognition system. Predictive tongue features are found to generate lower word error rates than those obtained from an auto-encoder without future frames, or from discrete cosine transforms. Improvement is apparent for the monophone/triphone Gaussian mixture model and deep neural network acoustic models. When tongue and lip modalities are combined, the advantage of the predictive features is reduced.

Convolutional neural network-based automatic classification of midsagittal tongue gestural targets using B-mode ultrasound images.

Tongue gestural target classification is of great interest to researchers in the speech production field. Recently, deep convolutional neural networks (CNN) have shown superiority to standard feature extraction techniques in a variety of domains. In this letter, both CNN-based speaker-dependent and speaker-independent tongue gestural target classification experiments are conducted to classify tongue gestures during natural speech production. The CNN-based method achieves state-of-the-art performance, even though no pre-training of the CNN (with the exception of a data augmentation preprocessing) was carried out.

A comparative study on the contour tracking algorithms in ultrasound tongue images with automatic re-initialization.

The feasibility of an automatic re-initialization of contour tracking is explored by using an image similarity-based method in the ultrasound tongue sequences. To this end, the re-initialization method was incorporated into current state-of-art tongue tracking algorithms, and a quantitative comparison was made between different algorithms by computing the mean sum of distances errors. The results demonstrate that with automatic re-initialization, the tracking error can be reduced from an average of 5-6 to about 4 pixels, a result obtained by using a large number of hand-labeled frames and similarity measurements to extract the contours, which results in improved performance.

Robust contour tracking in ultrasound tongue image sequences.

A new contour-tracking algorithm is presented for ultrasound tongue image sequences, which can follow the motion of tongue contours over long durations with good robustness. To cope with missing segments caused by noise, or by the tongue midsagittal surface being parallel to the direction of ultrasound wave propagation, active contours with a contour-similarity constraint are introduced, which can be used to provide 'prior' shape information. Also, in order to address accumulation of tracking errors over long sequences, we present an automatic re-initialization technique, based on the complex wavelet image similarity index. Experiments on synthetic data and on real 60 frame per second (fps) data from different subjects demonstrate that the proposed method gives good contour tracking for ultrasound image sequences even over durations of minutes, which can be useful in applications such as speech recognition where very long sequences must be analyzed in their entirety.

Arrhythmia discrimination in implantable cardioverter defibrillators using support vector machines applied to a new representation of electrograms.

Arrhythmia classification remains a major challenge for appropriate therapy delivery in implantable cardioverter defibrillators (ICDs). The purpose of this paper is to present a new algorithm for arrhythmia discrimination based on a statistical classification by support vector machines of a novel 2-D representation of electrograms (EGMs) named spatial projection of tachycardia (SPOT) EGMs. SPOT-based discrimination algorithm provided sensitivity and specificity of 98.8% and 91.3%, respectively, on a test database. A simplified version of the algorithm is also presented, which can be directly implemented in the ICD.

The time course of new T-wave ECG descriptors following single- and double-dose administration of sotalol in healthy subjects.

INTRODUCTION: The aim of the study was to assess the time course effect of IKr blockade on ECG biomarkers of ventricular repolarization and to evaluate the accuracy of a fully automatic approach for QT duration evaluation. METHODS: Twelve-lead digital ECG Holter was recorded in 38 healthy subjects (27 males, mean age = 27.4 + or - 8.0 years) on baseline conditions (day 0) and after administration of 160 mg (day 1) and 320 mg (day 2) of d-l sotalol. For each 24-hour period and each subject, ECGs were extracted every 10 minutes during the 4-hour period following drug dosage. Ventricular repolarization was characterized using three biomarker categories: conventional ECG time intervals, principal component analysis (PCA) analysis on the T wave, and fully automatic biomarkers computed from a mathematical model of the T wave. RESULTS: QT interval was significantly prolonged starting 1 hour 20 minutes after drug dosing with 160 mg and 1 hour 10 minutes after drug dosing with 320 mg. PCA ventricular repolarization parameters sotalol-induced changes were delayed (>3 hours). After sotalol dosing, the early phase of the T wave changed earlier than the late phase prolongation. Globally, the modeled surrogate QT paralleled manual QT changes. The duration of manual QT and automatic surrogate QT were strongly correlated (R(2) = 0.92, P < 0.001). The Bland and Altman plot revealed a nonstationary systematic bias (bias = 26.5 ms + or - 1.96*SD = 16 ms). CONCLUSIONS: Changes in different ECG biomarkers of ventricular repolarization display different kinetics after administration of a potent potassium channel blocker. These differences need to be taken into account when designing ventricular repolarization ECG studies.

A new setup for in vivo fluorescence imaging of photosynthetic activity.

Here, we describe a new imaging setup able to assess in vivo photosynthetic activity. The system specifically measures time-resolved chlorophyll fluorescence in response to light. It is composed of a fast digital camera equipped with a wide-angle lens for the analysis of samples up to 10 x 10 cm, i.e. entire plants or petri dishes. In the choice of CCD, we have opted for a 12-bits high frame rate [150 fps (frames per second)] at the expense of definition (640 x 480 pixels). Although the choice of digital camera is always a compromise between these two related features, we have designed a flexible system allowing the fast sampling of images (down to 100 micros) with a maximum spatial resolution. This image readout system, synchronized with actinic light and saturating pulses, allows a precise determination of F(0) and F(M), which is required to monitor PSII activity. This new imaging system, together with image processing techniques, is useful to investigate the heterogeneity of photosynthetic activity within leaves or to screen large numbers of unicellular algal mutant colonies to identify those with subtle changes in photosynthetic electron flow.

Automatic analysis of cardiac repolarization morphology using Gaussian mesa function modeling.

A novel fully automated method for wave identification and extraction from electrocardiogram (ECG) waveforms is presented. This approach implements the combined use of a new machine-learning algorithm and of specified parameterized functions called Gaussian mesa functions (GMFs). Individual cardiac cycle waveforms are broken up into GMFs using a generalized orthogonal forward regression algorithm; each individual GMF is subsequently identified (wave labeling) and analyzed for feature and morphologic extraction. The GMF associated with the repolarization waveform of the main vector lead, based on principal components analysis, was analyzed, and a set of morphologic parameters were derived under 2 experimental settings: first, in 100 digital 12-lead ECG Holter recordings acquired during three 24-hour periods (baseline and after 160 and 320 mg of sotalol) from 38 healthy subjects; second, in drug-free 12-lead resting ECGs from 100 genotyped long QT syndrome (LQTS) patients (50 each with LQT1 and LQT2). QT-interval duration was measured using an on-screen method applied to the global representative beats and reviewed by a senior cardiologist. QTci (individual correction) was used for analysis. All parameters in the sotalol test showed highly significant differences between the time of peak plasma concentration (Tmax) and baseline ECGs; however, the dynamic pattern of individual parameters followed different patterns. The LQTS test confirmed the results of the sotalol test, showing that GMF-based repolarization parameters were strongly modified as compared with healthy controls. In particular, T-wave width and descending phase of repolarization were more prolonged in LQT2 compared to LQT1.