Thank you for downloading using multivariate statistics. As you may know, people have look hundreds times for their favorite novels like this using multivariate statistics, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some harmful bugs inside their laptop. using multivariate statistics is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the using multivariate statistics is universally compatible with any devices to read.

Using Multivariate Statistics

The measurement of meaning

A wide variety of media can be used in learning, including distance learning, such as print, lectures, conference sections, tutors, pictures, video, sound, and computers. Any one instance of distance learning will make choices among these media, perhaps using several.

Multimedia in Learning

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

With digital equipment becoming increasingly networked, either on wired or wireless networks, for personal and professional use alike, distributed software systems have become a crucial element in information and communications technologies. The study of these systems forms the core of the ARLES' work, which is specifically concerned with defining new system software architectures, based on the use of emerging networking technologies. In this context, we concentrate on the study of distributed systems which bring to life the vision of ubiquitous computing systems, also known as ambient intelligence.

반도체 공정 overview

Spoken dialogue platforms usually log a multitude of information for each interaction between user and system. Such information is potentially very helpful for system evaluation and optimization; however, it is very difficult to interpret log data due to its large amount and specificity. In this chapter, we describe recent work to build a testbed for the analysis and evaluation of log-file information collected by telephone-based spoken dialogue platforms. On the signal level, parameters are extracted which allow recognition errors caused by channel and user characteristics to be analyzed. On the symbolic level, phonemic similarities are computed which allow the confusability of lexicon and grammar to be evaluated. The algorithms are integrated into a graphical user interface for an effective analysis of system performance. They are evaluated on data collected within a usability test of a pre-qualifying application of a large German telecom operator.

A new Testbed for Semi-Automatic Usability Evaluation and Optimization of Spoken Dialogue Systems

In our previous work, we derived the acoustic features, that contribute to the perception of warmth and competence in synthetic speech. As an extension, in our current work, we investigate the impact of the derived vocal features in the generation of the desired characteristics. The acoustic features, spectral ﬂux, F1 mean and F2 mean and their convex combinations were explored for the generation of higher warmth in female speech. The voiced slope, spectral ﬂux, and their convex combinations were investigated for the generation of higher competence in female speech. We have employed a feature quantization ap-proach in the traditional end-to-end tacotron based speech synthesis model. The listening tests have shown that the convex combination of acoustic features displays higher Mean Opinion Scores of warmth and competence when compared to that of individual features.

On incorporating social speaker characteristics in synthetic speech

Usability questionnaires usually contain scales related to effectiveness, efficiency and overall satisfaction which provide a quantitative value for the user’s opinion. However, analyzing quantitative data often does not show the reason underlying for a good or bad opinion. Simple questions like “What did you like about the system?” and “What did you not like about the system?” can shade light on the underlying reasons, but a lot of effort is needed for the analysis of such data. Nevertheless, the answers to these questions contain the users’ opinion in their own words and hence often show high correlation with the overall rating of the system. In the frame of the SpeechEval [1] project we analyzed the German VOICE Awards corpus over three consecutive years, categorizing the answers to these two free text questions and analyzing correlations between the categories and the overall rating of the systems. We used the data to build a general linear model for predicting the overall rating.

Say it as you mean it - analyzing free user comments in the VOICE awards corpus.

Je bessere Moglichkeiten bestehen, Umgebungen durch Einsatz von Sprachtechnologie „intelligent“ zu gestalten, desto starker steigt der Bedarf an einer schnellen und okonomischen Evaluierung. Ublicherweise umfasst eine adaquate Evaluierung zwei Aspekte: Die Uberprufung der Leistung der beteiligten Systemkomponenten (z.B. Spracherkennung, Sprachverstehen, Dialogfuhrung und Sprachausgabe), sowie die Quantifizierung verschiedener Qualitatsaspekte aus Benutzersicht, wie bspw. der Effizienz, des Komforts, der Gebrauchstauglichkeit sowie der Akzeptanz. Da sich Qualitat als Ergebnis eines Wahrnehmungsund Beurteilungsprozesses ergibt, bedarf es zur Messung der oben genannten Qualitatsaspekte i. Allg. kontrollierter (Labor-) Experimente mit Versuchspersonen.

/pdf/evaluierung-einer-intelligenten-hausumgebung-durch-437itgn7e6.pdf

Evaluierung einer intelligenten Hausumgebung durch Modellierung von Benutzerverhalten

In this paper, we present a new framework for the diagnostic prediction of transmitted speech quality. The idea is to extract perceptually relevant feature estimations from the speech signal, and combine them with an overall quality metric in order to obtain more reliable as well as more diagnostic predictions of speech quality. We implement this framework in two complementary ways: In terms of a signal-based model which can be used for online and offline measurement, and in terms of a parametric model which can be used for network planning. The implementations are compared to standard state-of-the-art models and show a similar level of reliability, while providing additional diagnostic value.

Sebastian Möller

Papers

A new Testbed for Semi-Automatic Usability Evaluation and Optimization of Spoken Dialogue Systems

On incorporating social speaker characteristics in synthetic speech

Say it as you mean it - analyzing free user comments in the VOICE awards corpus.

Evaluierung einer intelligenten Hausumgebung durch Modellierung von Benutzerverhalten

Diagnostic prediction of transmitted speech quality : a new framework for signal-based and parametric models