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Johannes Pittermann ,. Angela Pittermann. Tobias Herbig ,. Franz Gerl. Sakriani Sakti ,. Konstantin Markov. Satoshi Nakamura. Wolfgang Minker Editor ,.

What are spoken dialogue systems?

Michael Weber Editor. Hani Hagras Editor. Victor Callagan Editor. Tobias Heinroth Editor. Wolfgang Minker ,. Alex Waibel. Joseph Mariani.


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Spoken dialog systems

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Spoken Dialogue Systems Technology and Design

Fiction Non Fiction. Home Gardening International Subscriptions. Health Fitness International Subscriptions. Kids Girls. Would you like to rate and review this book? He was a visiting researcher at MIT from to He is interested in modeling for speech recognition and speech dialogue systems. Teruhisa Misu received the B. He worked on LSI designing, and broadband business development. He has been studying robotics and sensor fusion for interaction mechanism between human and robots at NTT Communication Science Laboratories.

He received a Doctorof-Engineering degree at Ruhr-University Bochum in for his work on the assessment and prediction of speech quality in telecommunications. He gained the qualification needed to be a professor venia legendi at the Faculty of Electrical Engineering and Information Technology at Ruhr-University Bochum in , with a book on the quality of telephone-based spoken dialogue systems. Satoshi Nakamura received his B. He was an associate professor of the graduate school of Information Science at Nara Institute of Science and Technology in He is ATR Fellow.

He launched the world first network-based commercial speech-to-speech translation service for 3-G mobile phones in Since he was an assistent professor at the Institute for Pattern Recognition in Erlangen.


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Since he is a full professor at the same institute and head of the speech group. His current interests are prosody, analysis of pathologic speech, computer aided language learning and emotion analysis. He received his Dr.

Multi-Task Spoken Dialog System (in Chinese)

His research focuses on dependency parsing of spoken Japanese, paraphrasing for language understanding, and proactive dialogue system. Combining xxi linguistic knowledge with signal processing skills he focussed on speech interpretation and automatic data- and metadata extraction. He gathered experience within the field of machine learning as exercised when recognizing human speech utterances and classifying emotional expression subliminal in speech, the latter of which became his M.

Among other works and publications, he recently developed an emotion recognition system that was successfully contributed to the international Emotion Challenge Benchmark, held at Interspeech The academic supervision during that time was done by Prof. Alexander Schmitt studied Computer Science in Ulm Germany and Marseille France with focus on media psychology and spoken language dialogue systems. He received his Masters degree in when graduating on Distributed Speech Recognition on mobile phones at Ulm University.

Schmitt works as research assistant at Ulm University and is currently carrying out his PhD research project under the supervision of Prof. Wolfgang Minker. He received his B. His research interests include robot language acquisition, spoken dialogue systems, machine learning, and sensor evolution. Currently, he has a scholarship by the Ministry of Science of the Spanish Goverment.

His research interests are speech recognition and dialogue systems. She studied Psychology and received her diploma degree in from the Chemnitz University of Technology. At T-Labs, she is working towards her PhD thesis.

Spoken Dialogue Systems Technology and Design - Semantic Scholar

He received his Ph. Currently, he is working on evaluating quality and usability of multimodal interfaces. The capabilities of the applied speech recognizer influence many other design criteria for spoken dialogue systems. In this chapter, the problem of multilingual speech for speech recognizers is analyzed and a solution for recognizing many languages simultaneously is proposed. Two main design goals for the described system were to keep the recognition feasible on embedded systems and to handle the native speech of the user with highest priority.

In addition, experiments are described that address the effect of non-native accent. With the help of the added multilinguality, existing in-car infotainment systems can be extended to international navigation input and music selection via voice.

Keywords: Speech recognition; Non-native; Embedded. Minker et al. One example are speech operated navigation systems that should allow destination input for many countries. In the near future companies are also preparing to launch music players that are operated via speech. A major problem in these applications is that the dialogue systems operate with constrained resources, and current speech recognition technology typically requires more resources for each language that has to be covered. As this chapter focuses mainly on speech recognition, it is necessary to understand what components are involved in a speech recognition system, and how it is linked to the spoken dialogue system.

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Therefore Figure 1 depicts a semi-continuous speech recognition system as it is applied in the experiments. Overview of a semi-continuous speech recognition system. The incoming speech signal is first reduced to a feature vector that contains the most relevant information. This vector is matched against Gaussians in the codebook, and the corresponding probabilities are passed to the decoder that considers three knowledge sources to find the most likely speech utterance that it passes to the speech dialogue system.

In our special case, we work with one codebook, i. This allows to evaluate the Gaussians separately in the vector quantization step, and keeps the total number of Gaussians limited, which is important for resource constrained systems. In this process the Gaussians are responsible for the judgment of individual feature vectors, and the HMM structure models the change of the feature vectors over time.

The other components like the lexicon and the language model represent additional knowledge sources that can help to retrieve correct decisions in case of almost equal hy- Multilingual Speech Interfaces for Resource-Constrained Dialogue Systems 3 potheses. For further details of speech recognition systems, see Huang et al.

The rest of this chapter is organized as follows. As we discuss quite a lot of literature from the field of speech recognition we decided to have a separate Section 2 for our literature review. Based on the findings from the literature, Section 3 gives a brief overview of the experiments and approaches in this work.

Section 4 then presents the experimental setup. Section 5 and 6 report experiments. The first one focuses on accent adaptation and the second one tests the combination of the most promising adaptation technique with a new method for the generation of multilingual acoustic models. Finally, Section 7 summarizes the findings from this chapter. In this approach, phonemes from different languages can share one acoustic model when they have the same IPA International Phonetic Alphabet, Ladefoged, symbol.

Examples are Koehler, ; Schultz and Waibel, ; Niesler, The sharing can also be based on acoustic model similarity determined by a distance measure. For example, Koehler and P.