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

Improving parsing of spontaneous speech with the help of prosodic boundaries

Parsing can be improved in automatic speech understanding if prosodic boundary marking is taken into account, because syntactic boundaries are often marked by prosodic means. Because large databases are needed for the training of statistical models for prosodic boundaries, we developed a labeling scheme for syntactic-prosodic boundaries within the German VERBMOBIL project (automatic speech-to-speech translation). We compare the results of classifiers (multi-layer perceptrons and language models) trained on these syntactic-prosodic boundary labels with classifiers trained on perceptual-prosodic and purely syntactic labels. Recognition rates of up to 96% were achieved. The turns that we need to parse consist of 20 words on the average and frequently contain sequences of partial sentence equivalents due to restarts, ellipsis, etc. For this material, the boundary scores computed by our classifiers can successfully be integrated into the syntactic parsing of word graphs; currently, they improve the parse time by 92% and reduce the number of parse trees by 96%. This is achieved by introducing a special Prosodic Syntactic Clause Boundary symbol (PSCB) into our grammar and guiding the search for the best word chain with the prosodic boundary scores

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

Feature extraction based on bio-inspired model for robust emotion recognition

Emotional state identification is an important issue to achieve more natural speech interactive systems. Ideally, these systems should also be able to work in real environments in which generally exist some kind of noise. Several bio-inspired representations have been applied to artificial systems for speech processing under noise conditions. In this work, an auditory signal representation is used to obtain a novel bio-inspired set of features for emotional speech signals. These characteristics, together with other spectral and prosodic features, are used for emotion recognition under noise conditions. Neural models were trained as classifiers and results were compared to the well-known mel-frequency cepstral coefficients. Results show that using the proposed representations, it is possible to significantly improve the robustness of an emotion recognition system. The results were also validated in a speaker independent scheme and with two emotional speech corpora.Fil: Albornoz, Enrique Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Rufiner, Hugo Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentin

An Assessment of Oral Health on the Pine Ridge Indian Reservation

An assessment on the oral health of 292 Oglala Lakota residents of the Pine Ridge Indian Reservation in South Dakota looks at dental issues, periodontal disease, oral lesions and need for dental care. The research was conducted by the University of Colorado, Center for Native Oral Health Research and funded by the W.K. Kellogg Foundation

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

Automatic Rating of Hoarseness by Text-based Cepstral and Prosodic Evaluation

The standard for the analysis of distorted voices is perceptual rating of read-out texts or spontaneous speech. Automatic voice evaluation, however, is usually done on stable sections of sustained vowels. In this paper, text-based and established vowel-based analysis are compared with respect to their ability to measure hoarseness and its subclasses. 73 hoarse patients (48.3±16.8 years) uttered the vowel /e/ and read the German version of the text “The North Wind and the Sun”. Five speech therapists and physicians rated roughness, breathiness, and hoarseness according to the German RBH evaluation scheme. The best human-machine correlations were obtained for measures based on the Cepstral Peak Prominence (CPP; up to |r | = 0.73). Support Vector Regression (SVR) on CPP-based measures and prosodic features improved the results further to r ≈0.8 and confirmed that automatic voice evaluation should be performed on a text recording