TY - CHAP A1 - Köhn, Arne A1 - Stegen, Florian A1 - Baumann, Timo T1 - Mining the Spoken Wikipedia for Speech Data and Beyond T2 - Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16) N2 - We present a corpus of time-aligned spoken data of Wikipedia articles as well as the pipeline that allows to generate such corpora for many languages. There are initiatives to create and sustain spoken Wikipedia versions in many languages and hence the data is freely available, grows over time, and can be used for automatic corpus creation. Our pipeline automatically downloads and aligns this data. The resulting German corpus currently totals 293h of audio, of which we align 71h in full sentences and another 86h of sentences with some missing words. The English corpus consists of 287h, for which we align 27h in full sentence and 157h with some missing words. Results are publically available. KW - Spoken Wikipedia KW - Long Audio Alignment KW - Speech Resource Y1 - 2016 UR - https://aclanthology.org/L16-1735 SP - 4644 EP - 4647 PB - European Language Resources Association (ELRA) ER - TY - CHAP A1 - Kirsch, Kolja A1 - Steinicke, Frank A1 - Baumann, Timo T1 - Entwicklung und Evaluierung einer Notenwender-App für Klaviernoten T2 - Mensch und Computer 2016 - Tagungsband, 4. - 7. September 2016, Aachen N2 - Das ständige Umblättern von Noten ist für Musiker ein wiederkehrendes Problem. Dieses wird häufig durch einen Assistenten des Musikers, dem sogenannten Notenwender, gelöst. Diese Unterstützung haben allerdings viele Musiker nur selten während des Übens. In diesem Artikel stellen wir eine Anwendung für mobile Geräte vor, die auf verschiedene Arten das Umblättern von Klavierpartituren unterstützt. In einer Studie mit professionellen Musikern und Klavierschülern wurden diese Arten gegeneinander abgewogen. Die Ergebnisse zeigen auf, dass computer-unterstütztes Blättern Vorteile gegenüber herkömmlichem Blättern hat. Y1 - 2016 U6 - https://doi.org/10.18420/muc2016-mci-0159 PB - Gesellschaft für Informatik e.V. CY - Aachen ER - TY - CHAP A1 - Twiefel, Johannes A1 - Baumann, Timo A1 - Heinrich, Stefan A1 - Wermter, Stefan T1 - Improving Domain-independent Cloud-based Speech Recognition with Domain-dependent Phonetic Post-processing T2 - AAAI 2014 – Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI-14), Québec, CA, July 27–31, 2014 N2 - Automatic speech recognition (ASR) technology has been developed to such a level that off-the-shelf distributed speech recognition services are available (free of cost), which allow researchers to integrate speech into their applications with little development effort or expert knowledge leading to better results compared with previously used open-source tools. Often, however, such services do not accept language models or grammars but process free speech from any domain. While results are very good given the enormous size of the search space, results frequently contain out-of-domain words or constructs that cannot be understood by subsequent domain-dependent natural language understanding (NLU) components. We present a versatile post-processing technique based on phonetic distance that integrates domain knowledge with open-domain ASR results, leading to improved ASR performance. Notably, our technique is able to make use of domain restrictions using various degrees of domain knowledge, ranging from pure vocabulary restrictions via grammars or N-Grams to restrictions of the acceptable utterances. We present results for a variety of corpora (mainly from human-robot interaction) where our combined approach significantly outperforms Google ASR as well as a plain open-source ASR solution. Y1 - 2014 UR - https://www2.informatik.uni-hamburg.de/wtm/publications/2014/TBHW14/Twiefel_AAAI2014_CR.pdf U6 - https://doi.org/10.1609/aaai.v28i1.8929 SP - 1529 EP - 1535 PB - Association for the Advancement of Artificial Intelligence ER - TY - CHAP A1 - Kennington, Casey A1 - Kousidis, Spyros A1 - Baumann, Timo A1 - Buschmeier, Hendrik A1 - Kopp, Stefan A1 - Schlangen, David ED - Miller, Erika ED - Wu, Yuqing T1 - Better Driving and Recall When In-car Information Presentation Uses Situationally-Aware Incremental Speech Output Generation T2 - Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI '14, Seattle WA, USA, September 17 - 19, 2014 N2 - It is established that driver distraction is the result of sharing cognitive resources between the primary task (driving) and any other secondary task. In the case of holding conversations, a human passenger who is aware of the driving conditions can choose to interrupt his speech in situations potentially requiring more attention from the driver, but in-car information systems typically do not exhibit such sensitivity. We have designed and tested such a system in a driving simulation environment. Unlike other systems, our system delivers information via speech (calendar entries with scheduled meetings) but is able to react to signals from the environment to interrupt when the driver needs to be fully attentive to the driving task and subsequently resume its delivery. Distraction is measured by a secondary short-term memory task. In both tasks, drivers perform significantly worse when the system does not adapt its speech, while they perform equally well to control conditions (no concurrent task) when the system intelligently interrupts and resumes. Y1 - 2014 UR - 978-1-4503-3212-5 U6 - https://doi.org/10.1145/2667317.2667332 SP - 1 EP - 7 PB - ACM CY - New York, NY ER - TY - CHAP A1 - Baumann, Timo T1 - Partial Representations Improve the Prosody of Incremental Speech Synthesis T2 - Interspeech 2014, Singapore, 14-18 September 2014 N2 - When humans speak, they do not plan their full utterance inall detail before beginning to speak, nor do they speak piece-by-piece and ignoring their full message – instead humans usepartial representations in which they fill in the missing partsas the utterance unfolds. Incremental speech synthesizers, incontrast, have not yet made use of partial representations and theinformation contained there-in.We analyze the quality of prosodic parameter assignments(pitch and duration) generated from partial utterance specifi-cations (substituting defaults for missing features) in order todetermine the requirements that symbolic incremental prosodymodelling should meet. We find that broader, higher-level infor-mation helps to improve prosody even if lower-level informationabout the near future is yet unavailable. Furthermore, we findthat symbolic phrase-level or utterance-level information is mosthelpful towards the end of the phrase or utterance, respectively,that is, when this information is becoming available even in theincremental case. Thus, the negative impact of incremental pro-cessing can be minimized by using partial representations thatare filled in incrementally. KW - incremental processing KW - prosody KW - speech synthesis KW - spoken dialogue systems KW - simultaneous interpreting Y1 - 2014 U6 - https://doi.org/10.21437/Interspeech.2014-199 SP - 2932 EP - 2936 ER - TY - CHAP A1 - Kousidis, Spyros A1 - Kennington, Casey A1 - Baumann, Timo A1 - Buschmeier, Hendrik A1 - Kopp, Stefan A1 - Schlangen, David ED - Ali Salah, Albert T1 - A Multimodal In-Car Dialogue System That Tracks The Driver's Attention T2 - Proceedings of the 16th International Conference on Multimodal Interaction, ICMI '14, November 12 - 16 2014, Istanbul N2 - When a passenger speaks to a driver, he or she is co-located with the driver, is generally aware of the situation, and can stop speaking to allow the driver to focus on the driving task. In-car dialogue systems ignore these important aspects, making them more distracting than even cell-phone conversations. We developed and tested a "situationally-aware" dialogue system that can interrupt its speech when a situation which requires more attention from the driver is detected, and can resume when driving conditions return to normal. Furthermore, our system allows driver-controlled resumption of interrupted speech via verbal or visual cues (head nods). Over two experiments, we found that the situationally-aware spoken dialogue system improves driving performance and attention to the speech content, while driver-controlled speech resumption does not hinder performance in either of these two tasks Y1 - 2014 SN - 9781450328852 U6 - https://doi.org/10.1145/2663204.2663244 SP - 26 EP - 33 PB - ACM CY - New York, NY ER - TY - CHAP A1 - Baumann, Timo A1 - Lindner, Felix ED - Tapus, Adriana ED - André, Elisabeth ED - Martin, Jean-Claude T1 - Incremental Speech Production for Polite and Natural Personal-Space Intrusion T2 - Social robotics : 7th International Conference, ICSR 2015, Paris, France, October 26-30, 2015, Proceedings T2 - Lecture notes in Computer science ; 9388 N2 - We propose to use a model of personal space to initiate communication while passing a human thereby acknowledging that humans are not just a special kind of obstacle to be avoided but potential interaction partners. As a simple form of interaction, our system communicates an apology while closely passing a human. To this end, we present a software architecture that integrates a social-spaces knowledge base and a component for incremental speech production. Incrementality ensures that the robot’s utterance can be adapted to fit the developing situation in a natural way. Observer ratings show that personal-space intrusion is perceived as both natural and polite if the robot has the capability to utter and adapt an apology in an incremental way whereas it is perceived as unfriendly if the robot intrudes personal space without saying anything. Moreover, the robot is perceived as less natural if it does not adapt. Y1 - 2015 SN - 978-3-319-25553-8 U6 - https://doi.org/10.1007/978-3-319-25554-5_8 SP - 72 EP - 82 PB - Springer CY - Cham ER - TY - CHAP A1 - Baumann, Timo A1 - Kennington, Casey A1 - Hough, Julian A1 - Schlangen, David T1 - Recognising Conversational Speech: What an Incremental ASR Should Do for a Dialogue System and How to Get There T2 - Proceedings of the International Workshop Series on Spoken Dialogue Systems Technology (IWSDS) 2016 N2 - Automatic speech recognition (ASR) is not only becoming increasingly accurate, but also increasingly adapted for producing timely, incremental output. However, overall accuracy and timeliness alone are insufficient when it comes to interactive dialogue systems which require stability in the output and responsivity to the utterance as it is unfolding. Furthermore, for a dialogue system to deal with phenomena such as disfluencies, to achieve deep understanding of user utterances these should be preserved or marked up for use by downstream components, such as language understanding, rather than be filtered out. Similarly, word timing can be informative for analyzing deictic expressions in a situated environment and should be available for analysis. Here we investigate the overall accuracy and incremental performance of three widely used systems and discuss their suitability for the aforementioned perspectives. From the differing performance along these measures we provide a picture of the requirements for incremental ASR in dialogue systems and describe freely available tools for using and evaluating incremental ASR. Y1 - 2016 ER - TY - CHAP A1 - Baumann, Timo A1 - Lindner, Felix T1 - Towards a Social Robot that Incrementally Justifies Personal-Space Intrusion T2 - Proceedings of the Workshop Towards a Framework for Joint Action 2015 N2 - Robots should appropriately give reasons for their actions when these actions affect a human’s action or goal space. Communicating reasons may help the human understand the robot’s intents and may initiate joint action, i. e., accepting the robot’s goals and cooperating on the robot’s actions. However, to be efficient, the communication of reasons should be limited to the necessary rather than to completeness, conforming to the Gricean Maxim of Quantity. Furthermore, what is necessary only becomes apparent as the situation evolves and hence, for seamless interaction, ongoing utterances must be adapted as they happen. We present a system that flexibly gives reasons in a reduced setting in which the robot needs to intrude a human’s personal space in order to reach its goal. Y1 - 2015 UR - https://fja.sciencesconf.org/conference/fja/TB_FL_joint_action_abstract.pdf ER - TY - CHAP A1 - Pouget, Maël A1 - Hueber, Thomas A1 - Bailly, Gérard A1 - Baumann, Timo T1 - HMM training strategy for incremental speech synthesis T2 - Interspeech 2015, Dresden, Germany, 6-10 September 2015 N2 - Incremental speech synthesis aims at delivering the synthetic voice while the sentence is still being typed. One of the main challenges is the online estimation of the target prosody from a partial knowledge of the sentence's syntactic structure. In the context of HMM-based speech synthesis, this typically results in missing segmental and suprasegmental features, which describe the linguistic context of each phoneme. This study describes a voice training procedure which integrates explicitly a potential uncertainty on some contextual features. The proposed technique is compared to a baseline approach (previously published), which consists in substituting a missing contextual feature by a default value calculated on the training set. Both techniques were implemented in a HMM-based Text-To-Speech system for French, and compared using objective and perceptual measurements. Experimental results show that the proposed strategy outperforms the baseline technique for this language. KW - HMM-based speech synthesis KW - incremental KW - TTS KW - HTS KW - prosody Y1 - 2015 U6 - https://doi.org/10.21437/Interspeech.2015-304 SP - 1201 EP - 1205 PB - ISCA ER - TY - CHAP A1 - Baumann, Timo T1 - Decision Tree Usage for Incremental Parametric Speech Synthesis T2 - Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2014, 4-9 May 2014, Florence, Italy N2 - Human speakers plan and deliver their utterances incrementally, piece-by-piece, and it is obvious that their choice regarding phonetic details (and the details' peculiarities) is rarely determined by globally optimal solutions. In contrast, parametric speech synthesizers use a full-utterance context when optimizing vocoding parameters and when determing HMM states. Apart from being cognitively implausible, this impedes incremental use-cases, where the future context is often at least partially unavailable. This paper investigates the `locality' of features in parametric speech synthesis voices and takes some missing steps towards better HMM state selection and prosody modelling for incremental speech synthesis. KW - Speech Synthesis KW - Incremental Processing KW - HMM Synthesis KW - Interactivity KW - Spoken Dialogue Systems Y1 - 2014 U6 - https://doi.org/10.1109/ICASSP.2014.6854316 SP - 3847 EP - 3851 PB - IEEE ER - TY - CHAP A1 - Baumann, Timo A1 - Bangalore, Srinivas A1 - Hirschberg, Julia ED - Federico, Marcello ED - Stüker, Sebastian ED - Yvon, François T1 - Towards Simultaneous Interpreting: The Timing of Incremental Machine Translation and Speech Synthesis T2 - Proceedings of the 11th International Workshop on Spoken Language Translation. Papers. Lake Tahoe, December 4th and 5th, 2014 N2 - In simultaneous interpreting, human experts incrementally construct and extend partial hypotheses about the source speaker’s message, and start to verbalize a corresponding message in the target language, based on a partial translation – which may have to be corrected occasionally. They commence the target utterance in the hope that they will be able to finish understanding the source speaker’s message and determine its translation in time for the unfolding delivery. Of course, both incremental understanding and translation by humans can be garden-pathed, although experts are able to optimize their delivery so as to balance the goals of minimal latency, translation quality and high speech fluency with few corrections. We investigate the temporal properties of both translation input and output to evaluate the tradeoff between low latency and translation quality. In addition, we estimate the improvements that can be gained with a tempo-elastic speech synthesizer. KW - incremental processing KW - speech synthesis KW - timing KW - simultaneous interpreting Y1 - 2014 UR - https://aclanthology.org/2014.iwslt-papers.2.pdf SP - 163 EP - 168 ER -