A Context-based Numeral Reading Technique for Text to Speech Systems

This paper presents a novel technique for context based numeral reading in Indian language text to speech systems. The model uses a set of rules to determine the context of the numeral pronunciation and is being integrated with the waveform concatenation technique to produce speech out of the input text in Indian languages. For this purpose, the three Indian languages Odia, Hindi and Bengali are considered. To analyze the performance of the proposed technique, a set of experiments are performed considering different context of numeral pronunciations and the results are compared with existing syllable-based technique. The results obtained from different experiments shows the effectiveness of the proposed technique in producing intelligible speech out of the entered text utterances compared to the existing technique even with very less storage and execution time

ALEC: Active learning with ensemble of classifiers for clinical diagnosis of coronary artery disease

Invasive angiography is the reference standard for coronary artery disease (CAD) diagnosis but is expensive and associated with certain risks. Machine learning (ML) using clinical and noninvasive imaging parameters can be used for CAD diagnosis to avoid the side effects and cost of angiography. However, ML methods require labeled samples for efficient training. The labeled data scarcity and high labeling costs can be mitigated by active learning. This is achieved through selective query of challenging samples for labeling. To the best of our knowledge, active learning has not been used for CAD diagnosis yet. An Active Learning with Ensemble of Classifiers (ALEC) method is proposed for CAD diagnosis, consisting of four classifiers. Three of these classifiers determine whether a patient’s three main coronary arteries are stenotic or not. The fourth classifier predicts whether the patient has CAD or not. ALEC is first trained using labeled samples. For each unlabeled sample, if the outputs of the classifiers are consistent, the sample along with its predicted label is added to the pool of labeled samples. Inconsistent samples are manually labeled by medical experts before being added to the pool. The training is performed once more using the samples labeled so far. The interleaved phases of labeling and training are repeated until all samples are labeled. Compared with 19 other active learning algorithms, ALEC combined with a support vector machine classifier attained superior performance with 97.01% accuracy. Our method is justified mathematically as well. We also comprehensively analyze the CAD dataset used in this paper. As part of dataset analysis, features pairwise correlation is computed. The top 15 features contributing to CAD and stenosis of the three main coronary arteries are determined. The relationship between stenosis of the main arteries is presented using conditional probabilities. The effect of considering the number of stenotic arteries on sample discrimination is investigated. The discrimination power over dataset samples is visualized, assuming each of the three main coronary arteries as a sample label and considering the two remaining arteries as sample features

Data bases and data base systems related to NASA's Aerospace Program: A bibliography with indexes

This bibliography lists 641 reports, articles, and other documents introduced into the NASA scientific and technical information system during the period January 1, 1981 through June 30, 1982. The directory was compiled to assist in the location of numerical and factual data bases and data base handling and management systems

Combining acoustic and linguistic features in phrase-oriented prosody prediction

Paper presented at Speech Prosody 8, 2016 May 31 - Jun 3; Boston, United States.Intonation is traditionally considered to be the most important prosodic feature, whereupon an important research effort has been devoted to automatic segmentation and labeling of speech samples to grasp intonation cues. A number of studies also show that when duration or intensity are incorporated, automatic prosody labeling is further improved. However, the combination of word level acoustic features still attains poor results when machine learning techniques are applied on annotated corpora to derive intonation for speech synthesis applications. To address this problem, we present an experimental set-up for the development of a hierarchical prosodic structure model which combines linguistic features, including information structure, and three acoustic elements (intensity, pitch and duration). We show empirically that this combination leads to a considerably more accurate representation of prosody and, consequently, a more reliable automatic labeling of speech corpora for machine learning.This work is part of a project that has received funding from the European Union’s Horizon 2020 Research and Innovation/nProgramme under the Grant Agreement number H2020-RIA-645012. The second author is partially funded by a grant from/nthe Spanish Ministry of Economy and Competitivity in the framework of the Juan de la Cierva fellowship program

Combining acoustic and linguistic features in phrase-oriented prosody prediction

Paper presented at Speech Prosody 8, 2016 May 31 - Jun 3; Boston, United States.Intonation is traditionally considered to be the most important prosodic feature, whereupon an important research effort has been devoted to automatic segmentation and labeling of speech samples to grasp intonation cues. A number of studies also show that when duration or intensity are incorporated, automatic prosody labeling is further improved. However, the combination of word level acoustic features still attains poor results when machine learning techniques are applied on annotated corpora to derive intonation for speech synthesis applications. To address this problem, we present an experimental set-up for the development of a hierarchical prosodic structure model which combines linguistic features, including information structure, and three acoustic elements (intensity, pitch and duration). We show empirically that this combination leads to a considerably more accurate representation of prosody and, consequently, a more reliable automatic labeling of speech corpora for machine learning.This work is part of a project that has received funding from the European Union’s Horizon 2020 Research and Innovation/nProgramme under the Grant Agreement number H2020-RIA-645012. The second author is partially funded by a grant from/nthe Spanish Ministry of Economy and Competitivity in the framework of the Juan de la Cierva fellowship program