Natural Language Processing Springer

From common consumer products such as cell phones and MP3 players to more sophisticated projects such as human-machine interfaces and responsive robots, speech technologies are now everywhere. Many think that it is just a matter of time before more applications of the science of speech become inescapable in our daily life. This handbook is meant to play a fundamental role for sustainable progress in speech research and development. Springer Handbook of Speech Processing targets three categories of readers: graduate students, professors and active researchers in academia and research labs, and engineers in industry who need to understand or implement some specific algorithms for their speech-related products.

Mit diesem Herausgeberwerk führen die Autoren den Begriff „Big Data Analytics“ ein und geben Fallstudien aus unterschiedlichen Anwendungsgebieten. Unter Big Data Analytics wird das Aufbereiten, Analysieren und Interpretieren von großen, oft heterogenen Datenbeständen verstanden, mit dem Ziel, Muster und Zusammenhänge in den Daten aufzudecken und Entscheidungsgrundlagen für wissenschaftliche, betriebliche oder gesellschaftliche Fragestellungen zu erhalten.

This book continues a series of Springer publications devoted to the emerging field of Integrated Artificial Intelligence and Machine Learning with Visual Knowledge Discovery and Visual Analytics that combine advances in both fields. Artificial Intelligence and Machine Learning face long-standing challenges of explainability and interpretability that underpin trust. Such attributes are fundamental to both decision-making and knowledge discovery. Models are approximations and, at best, interpretations of reality that are transposed to algorithmic form. A visual explanation paradigm is critically important to address such challenges, as current studies demonstrate in salience analysis in deep learning for images and texts. Visualization means are generally effective for discovering and explaining high-dimensional patterns in all high-dimensional data, while preserving data properties and relations in visualizations is challenging. Recent developments, such as in General Line Coordinates, open new opportunities to address such challenges.