Prosody takes over : towards a prosodically guided dialog system

The domain of the speech recognition and dialog system EVAR is train time table inquiry. We observed that in real human-human dialogs when the officer transmits the information, the customer very often interrupts. Many of these interruptions are just repetitions of the time of day given by the officer. The functional role of these interruptions is often determined by prosodic cues only. An important result of experiments where naive persons used the EVAR system is that it is hard to follow the train connection given via speech synthesis. In this case it is even more important than in human-human dialogs that the user has the opportunity to interact during the answer phase. Therefore we extended the dialog module to allow the user to repeat the time of day and we added a prosody module guiding the continuation of the dialog by analyzing the intonation contour of this utterance.Der Diskursbereich des Spracherkennungs- und Dialogsystems EVAR ist Fahrplanauskunft für Züge. Wir beobachteten, dass in realen Mensch-Mensch Dialogen der Kunde sehr oft den Auskunftsbeamten unterbricht, wenn dieser die Information übermittelt. Viele dieser Unterbrechungen sind ausschließlich Wiederholungen der Uhrzeitangabe des Beamten. Die funktionale Rolle dieser Unterbrechungen wird häufig alleine durch prosodische Mittel bestimmt. Ein wichtiges Ergebnis von Dialog Experimenten mit naiven Personen ergab, dass es schwer ist, den Verbindungsauskünften von EVAR via Sprachsynthese zu folgen. In diesem Fall ist es sogar noch wichtiger als in Mensch-Mensch Dialogen, dass der Benutzer die Möglichkeit hat, während der Antwortphase zu interagieren. Deshalb haben wir das Dialogmodul erweitert, um dem Benutzer die Möglichkeit zu geben, die Uhrzeitangaben zu wiederholen, und wir fügten ein Prosodiemodul hinzu, das die Fortführung des Dialogs steuert, indem die Intonation dieser Äußerung analysiert wir

Prosody takes over : a prosodically guided dialog system

In this paper first experiments with naive persons using the speech understanding and dialog system EVAR are discussed. The domain of EVAR is train table inquiry. We observed that in real human-human dialogs when the officer transmits the information the customer very often interrupts. Many of these interruptions are just repetitions of the time of day given by the officer. The functional role of these interruptions is determined by prosodic cues only. An important result of the experiments with EVAR is that it is hard to follow the system giving the train connection via speech synthesis. In this case it is even more important than in human-human dialogs that the user has the opportunity to interact during the answer phase. Therefore we extended the dialog module to allow the user to repeat the time of day and we added a prosody module guiding the continuation of the dialog

An integrated model of acoustics and language using semantic classification trees

We propose Multi-level Semantic Classication Trees to combine different information sources for predicting speech events (e.g. word chains, phrases, etc.) Traditionally in speech recognition systems these information sources (acoustic evidence, language model) are calculated independently and combined via Bayes rule. The proposed approach allows one to combine sources of different types - is no longer necessary for each source to yield a probability. Moreover the tree can look at several information sources simultaneously. The approach is demonstrated for the prediction of prosodically marked phrase boundaries, combining information about the spoken word chain, word category information, prosodic parameters, and the result of a neural network predicting the boundary on the basis of acoustic-prosodic features. The recognition rates of up to 90% for the two class problem boundary vs. no boundary are already comparable to results achieved with the above mentioned Bayes rule approach that combines the acoustic classifier with a 5-gram categorical language model. This is remarkable, since so far only a small set of questions combining information from different sources have been implemented

Prosody takes over : towards a prosodically guided dialog system

The domain of the speech recognition and dialog system EVAR is train time table inquiry. We observed that in real human-human dialogs when the officer transmits the information, the customer very often interrupts. Many of these interruptions are just repetitions of the time of day given by the officer. The functional role of these interruptions is often determined by prosodic cues only. An important result of experiments where naive persons used the EVAR system is that it is hard to follow the train connection given via speech synthesis. In this case it is even more important than in human-human dialogs that the user has the opportunity to interact during the answer phase. Therefore we extended the dialog module to allow the user to repeat the time of day and we added a prosody module guiding the continuation of the dialog by analyzing the intonation contour of this utterance.Der Diskursbereich des Spracherkennungs- und Dialogsystems EVAR ist Fahrplanauskunft für Züge. Wir beobachteten, dass in realen Mensch-Mensch Dialogen der Kunde sehr oft den Auskunftsbeamten unterbricht, wenn dieser die Information übermittelt. Viele dieser Unterbrechungen sind ausschließlich Wiederholungen der Uhrzeitangabe des Beamten. Die funktionale Rolle dieser Unterbrechungen wird häufig alleine durch prosodische Mittel bestimmt. Ein wichtiges Ergebnis von Dialog Experimenten mit naiven Personen ergab, dass es schwer ist, den Verbindungsauskünften von EVAR via Sprachsynthese zu folgen. In diesem Fall ist es sogar noch wichtiger als in Mensch-Mensch Dialogen, dass der Benutzer die Möglichkeit hat, während der Antwortphase zu interagieren. Deshalb haben wir das Dialogmodul erweitert, um dem Benutzer die Möglichkeit zu geben, die Uhrzeitangaben zu wiederholen, und wir fügten ein Prosodiemodul hinzu, das die Fortführung des Dialogs steuert, indem die Intonation dieser Äußerung analysiert wir

An integrated model of acoustics and language using semantic classification trees

We propose Multi-level Semantic Classication Trees to combine different information sources for predicting speech events (e.g. word chains, phrases, etc.) Traditionally in speech recognition systems these information sources (acoustic evidence, language model) are calculated independently and combined via Bayes rule. The proposed approach allows one to combine sources of different types - is no longer necessary for each source to yield a probability. Moreover the tree can look at several information sources simultaneously. The approach is demonstrated for the prediction of prosodically marked phrase boundaries, combining information about the spoken word chain, word category information, prosodic parameters, and the result of a neural network predicting the boundary on the basis of acoustic-prosodic features. The recognition rates of up to 90% for the two class problem boundary vs. no boundary are already comparable to results achieved with the above mentioned Bayes rule approach that combines the acoustic classifier with a 5-gram categorical language model. This is remarkable, since so far only a small set of questions combining information from different sources have been implemented

Establishing standard measures of growth in learning progress assessment

In diesem Kurzbeitrag schildern wir Herausforderungen bei der Normierung von Verfahren zur Lernverlaufsdiagnostik, die sich bei der Statusdiagnostik in dieser Form nicht stellen. Diese betreffen insbesondere die Frage, ob Normen für regulären Unterricht oder intensive Förderung benötigt werden, aber auch die Unterschiedlichkeit von Lernzuwächsen in Abhängigkeit von der erfassten Kompetenz, des verwendeten Messverfahrens, des Untersuchungszeitraums und bestimmter Schülermerkmale. Darüber hinaus weisen Lernverläufe im Unterschied zu einmaligen Testungen die statistische Besonderheit auf, dass die Größe der Vertrauensintervalle für den Lernzuwachs von der Anzahl der verfügbaren Messungen abhängt. Basierend auf einer Analyse dieser Herausforderungen schlagen wir Designmerkmale und Analyseschritte bei der Normierung von Verfahren zur Lernverlaufsdiagnostik vor. (DIPF/Orig.)The standardization of growth in learning progress assessment faces challenges that go beyond those of typical achievement tests. These challenges concern the question whether norm values refer to business-as-usual instruction or effective treatments, and the variability of learning growth as a function of the respective competence, assessment, seasonal effects, and student characteristics. Moreover, in contrast to confidence intervals of single assessments, confidence intervals of slopes are statistically specific in that they depend on the number of assessments. Based on these challenges, we propose design features and steps for the analysis when establishing standard measures of growth for learning progress assessment. (DIPF/Orig.

Industrialisierung in Oberschwaben und am Bodensee: Beitr. u. Daten zur Entwicklung von Bevoelkerung, Agrarstruktur, Industrie, Berufstaetigkeit, Wahlverhalten, Arbeiterbewegung u. Lebenshaltungskosten

SIGLEAvailable from Bibliothek des Instituts fuer Weltwirtschaft, ZBW, Duesternbrook Weg 120, D-24105 Kiel C 137458 / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman

Von Grethaus und Salzstadel zur Kreissparkasse: Texte u. Bilder zur Buchhorn-Friedrichshafener Stadtgeschichte, zsgest. von Elmar L. Kuhn zu e. Ausstellung d. Kreissparkasse Friedrichshafen

Available from Bibliothek des Instituts fuer Weltwirtschaft, ZBW, Duesternbrook Weg 120, D-24105 Kiel C 137311 / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDEGerman