@inproceedings{PougetHueberBaillyetal., author = {Pouget, Ma{\"e}l and Hueber, Thomas and Bailly, G{\´e}rard and Baumann, Timo}, title = {HMM training strategy for incremental speech synthesis}, series = {Interspeech 2015, Dresden, Germany, 6-10 September 2015}, booktitle = {Interspeech 2015, Dresden, Germany, 6-10 September 2015}, publisher = {ISCA}, doi = {10.21437/Interspeech.2015-304}, pages = {1201 -- 1205}, abstract = {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.}, language = {en} } @inproceedings{Baumann, author = {Baumann, Timo}, title = {Decision Tree Usage for Incremental Parametric Speech Synthesis}, series = {Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2014, 4-9 May 2014, Florence, Italy}, booktitle = {Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2014, 4-9 May 2014, Florence, Italy}, publisher = {IEEE}, doi = {10.1109/ICASSP.2014.6854316}, pages = {3847 -- 3851}, abstract = {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.}, language = {en} } @inproceedings{BaumannBangaloreHirschberg, author = {Baumann, Timo and Bangalore, Srinivas and Hirschberg, Julia}, title = {Towards Simultaneous Interpreting: The Timing of Incremental Machine Translation and Speech Synthesis}, series = {Proceedings of the 11th International Workshop on Spoken Language Translation. Papers. Lake Tahoe, December 4th and 5th, 2014}, booktitle = {Proceedings of the 11th International Workshop on Spoken Language Translation. Papers. Lake Tahoe, December 4th and 5th, 2014}, editor = {Federico, Marcello and St{\"u}ker, Sebastian and Yvon, Fran{\c{c}}ois}, pages = {163 -- 168}, abstract = {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.}, language = {en} } @article{MeyerSickendiekHusseinBaumann, author = {Meyer-Sickendiek, Burkhard and Hussein, Hussein and Baumann, Timo}, title = {Towards the Creation of a Poetry Translation Mapping System}, series = {Archives of Data Science, Series A}, volume = {5}, journal = {Archives of Data Science, Series A}, number = {1}, publisher = {Towards the Creation of a Poetry Translation Mapping System}, issn = {2363-9881}, doi = {10.5445/KSP/1000087327/21}, pages = {1 -- 15}, abstract = {The translation of poetry is a complex, multifaceted challenge: the translated text should communicate the same meaning, similar metaphoric expressions, and also match the style and prosody of the original poem. Research on machine poetry translation is existing since 2010, but for four reasons it is still rather insufficient: 1. The few approaches existing completely lack any knowledge about current developments in both lyric theory and translation theory. 2. They are based on very small datasets. 3. They mostly ignored the neural learning approach that superseded the long-standing dominance of phrase-based approaches within machine translation. 4. They have no concept concerning the pragmatic function of their research and the resulting tools. Our paper describes how to improve the existing research and technology for poetry translations in exactly these four points. With regards to 1) we will describe the "Poetics of Translation". With regards to 2) we will introduce the Worlds largest corpus for poetry translations from lyrikline. With regards to 3) we will describe first steps towards a neural machine translation of poetry. With regards to 4) we will describe first steps towards the development of a poetry translation mapping system.}, language = {en} } @misc{WardBaumannKarkhedkaretal., author = {Ward, Nigel and Baumann, Timo and Karkhedkar, Shreyas and Novick, David}, title = {Dynamic control of voice codec data rate}, abstract = {A method, system, and computer-usable non-transitory storage device for dynamic voice codec adaptation are disclosed. The voice codec adapts in real time to devote more bits to audio quality when it is most needed, and fewer bits to less important parts of utterances are disclosed. Dialog knowledge is utilized for compression opportunities to adjust the bitrate moment-by-moment, based on the inferred value of each frame. Frame importance and appropriate transmission fidelity is predicted based on prosodic features and models of dialog dynamics. This technique provides the same communications quality with less spectrum needs, fewer antennas, and less battery drain.}, language = {en} } @inproceedings{JohannssenBiemannRemusetal., author = {Johannßen, Dirk and Biemann, Chris and Remus, Steffen and Baumann, Timo and Scheffer, David}, title = {GermEval 2020 Task 1 on the Classification and Regression of Cognitive and Motivational style from Text}, series = {Proceedings of the 5th SwissText \& 16th KONVENS Joint Conference 2020}, booktitle = {Proceedings of the 5th SwissText \& 16th KONVENS Joint Conference 2020}, address = {Zurich, Switzerland}, pages = {1 -- 10}, abstract = {This paper describes the tasks, databases, baseline systems, and summarizes submissions and results for the GermEval 2020 Shared Task 1 on the Classification and Regression of Cognitive and Motivational Style from Text. This shared task is divided into two subtasks, a regression task, and a classification task. Subtask 1 asks participants to reproduce a ranking of students based on average aptitude indicators such as different high school grades and different IQ scores. The second subtask aims to classify so-called implicit motives, which are projective testing procedures that can reveal unconscious desires. Besides five implicit motives, the target labels of Subtask 2 also contain one of six levels that describe the type of self-regulation when acting out a motive, which makes this task a multiclass-classification with 30 target labels. 3 participants submitted multiple systems. Subtask 1 was solved (best r = .3701) mainly with non-neural systems and statistical language representations, submissions for Subtask 2 utilized neural approaches and word embeddings (best macro F1 = 70.40). Not only were the tasks solvable, analyses by the participants even showed connections to the implicit psychometrics theory and behavioral observations made by psychologists. This paper describes the tasks, databases, baseline systems, and summarizes submissions and results for the GermEval 2020 Shared Task 1 on the Classification and Regression of Cognitive and Motivational Style from Text. This shared task is divided into two subtasks, a regression task, and a classification task. Subtask 1 asks participants to reproduce a ranking of students based on average aptitude indicators such as different high school grades and different IQ scores. The second subtask aims to classify so-called implicit motives, which are projective testing procedures that can reveal unconscious desires. Besides five implicit motives, the target labels of Subtask 2 also contain one of six levels that describe the type of self-regulation when acting out a motive, which makes this task a multiclass-classification with 30 target labels. 3 participants submitted multiple systems. Subtask 1 was solved (best r =.3701) mainly with non-neural systems and statistical language representations, submissions for Subtask 2 utilized neural approaches and word embeddings (best macro F1 = 70.40). Not only were the tasks solvable, analyses by the participants even showed connections to the implicit psychometrics theory and behavioral observations made by psychologists.}, language = {en} } @inproceedings{HusseinMeyerSickendiekBaumann, author = {Hussein, Hussein and Meyer-Sickendiek, Burkhard and Baumann, Timo}, title = {Free Verse and Beyond: How to Classify Post-modern Spoken Poetry}, series = {Proceedings of Speech Prosody: Tokyo, Japan, 25-28 May 2020}, booktitle = {Proceedings of Speech Prosody: Tokyo, Japan, 25-28 May 2020}, publisher = {ISCA}, doi = {10.21437/SpeechProsody.2020-141}, pages = {690 -- 694}, abstract = {This paper presents the classification of rhythmical patterns detected in post-modern spoken poetry by means of machine learning algorithms that use manually engineered features or automatically learnt representations. We used the world's largest corpus of spoken poetry from our partner lyrikline. We identified nine rhythmical patterns within a spectrum raging from a more fluent to a more disfluent poetic style. The text data analyzed by a statistical parser. Prosodic features of rhythmical patterns are identified by using the parser information. For the classification of rhythmical patterns, we used a neural networks-based approach which use text, audio, and pause information between poetic lines as features. Different combinations of features as well as the integration of feature engineering in the neural networks-based approach are tested. We compared the performance of both approaches (feature-based and neural network-based) using combinations of different features. The results show - by using the weighted average of f-measure for the evaluation - that the neural networks-based approach performed much better in classification of rhythmical patterns. The important improvement of the classification results lies in the use of the audio information. The integration of feature engineering in the neural networks-based approach yielded a very small result improvement.}, language = {en} } @inproceedings{BaumannMeyerSickendiek, author = {Baumann, Timo and Meyer-Sickendiek, Burkhard}, title = {Deep Learning meets Post-modern Poetry}, series = {TwinTalks 2020: Understanding and Facilitating Collaboration in Digital Humanities 2020, proceedings of the Twin Talks 2 and 3 workshops at DHN 2020 and DH 2020, Ottawa Canada and Riga Latvia, July 23 and October 20, 2020}, booktitle = {TwinTalks 2020: Understanding and Facilitating Collaboration in Digital Humanities 2020, proceedings of the Twin Talks 2 and 3 workshops at DHN 2020 and DH 2020, Ottawa Canada and Riga Latvia, July 23 and October 20, 2020}, editor = {Krauwer, Steven and Fišer, Darja}, publisher = {RWTH Aachen}, issn = {1613-0073}, pages = {30 -- 36}, abstract = {We summarize our project Rhythmicalizer in which we analyze a corpus of post-modern poetry in a combination of qualitative hermeneutical and computational methods, as we have run the project over the course of the past three years (and preparing it for some time before that). Interdisciplinary work is always challenging and we here focus on some of the highlights of our collaboration.}, language = {en} } @inproceedings{Baumann, author = {Baumann, Timo}, title = {How a Listener Influences the Speaker}, series = {Proceedings of Speech Prosody: Tokyo, Japan, 25-28 May 2020}, booktitle = {Proceedings of Speech Prosody: Tokyo, Japan, 25-28 May 2020}, publisher = {ISCA}, doi = {10.21437/SpeechProsody.2020-198}, pages = {970 -- 974}, abstract = {Listeners typically provide feedback while listening to a speaker in conversation and thereby engage in the co-construction of the interaction. We analyze the influence of the listener on the speaker by investigating how her verbal feedback signals help in modeling the speaker's language. We find that feedback from the listener may help in modeling the speaker's language, whether through the listener's feedback as transcribed, or the acoustic signal directly. We find the largest positive effects for end of sentence as well as for pauses mid-utterance, but also effects that indicate we successfully model elaborations of ongoing utterances that may result from the presence or absence of listener feedback.}, language = {en} } @article{BaumannKoehnHennig, author = {Baumann, Timo and K{\"o}hn, Arne and Hennig, Felix}, title = {The Spoken Wikipedia Corpus collection: Harvesting, alignment and an application to hyperlistening}, series = {Language Resources and Evaluation}, volume = {53}, journal = {Language Resources and Evaluation}, number = {2}, publisher = {Springer Nature}, doi = {10.1007/s10579-017-9410-y}, pages = {303 -- 329}, abstract = {Spoken corpora are important for speech research, but are expensive to create and do not necessarily reflect (read or spontaneous) speech 'in the wild'. We report on our conversion of the preexisting and freely available Spoken Wikipedia into a speech resource. The Spoken Wikipedia project unites volunteer readers of Wikipedia articles. There are initiatives to create and sustain Spoken Wikipedia versions in many languages and hence the available data grows over time. Thousands of spoken articles are available to users who prefer a spoken over the written version. We turn these semi-structured collections into structured and time-aligned corpora, keeping the exact correspondence with the original hypertext as well as all available metadata. Thus, we make the Spoken Wikipedia accessible for sustainable research. We present our open-source software pipeline that downloads, extracts, normalizes and text-speech aligns the Spoken Wikipedia. Additional language versions can be exploited by adapting configuration files or extending the software if necessary for language peculiarities. We also present and analyze the resulting corpora for German, English, and Dutch, which presently total 1005 h and grow at an estimated 87 h per year. The corpora, together with our software, are available via http://islrn.org/resources/684-927-624-257-3/. As a prototype usage of the time-aligned corpus, we describe an experiment about the preferred modalities for interacting with information-rich read-out hypertext. We find alignments to help improve user experience and factual information access by enabling targeted interaction.}, language = {en} } @inproceedings{SabooBaumann, author = {Saboo, Ashutosh and Baumann, Timo}, title = {Integration of Dubbing Constraints into Machine Translation}, series = {Proceedings of the Fourth Conference on Machine Translation, Volume 1: Research Papers}, booktitle = {Proceedings of the Fourth Conference on Machine Translation, Volume 1: Research Papers}, editor = {Bojar, Ondřej and Chatterjee, Rajen and Federmann, Christian and Fishel, Mark and Graham, Yvette and Haddow, Barry and Huck, Matthias and Yepes, Antonio Jimeno and Koehn, Philipp and Martins, Andr{\´e} and Monz, Christof and Negri, Matteo and N{\´e}v{\´e}ol, Aur{\´e}lie and Neves, Mariana and Post, Matt and Turchi, Marco and Verspoor, Karin}, publisher = {Association for Computational Linguistics}, address = {Stroudsburg, PA, USA}, doi = {10.18653/v1/W19-5210}, pages = {94 -- 101}, abstract = {Translation systems aim to perform a meaning-preserving conversion of linguistic material (typically text but also speech) from a source to a target language (and, to a lesser degree, the corresponding socio-cultural contexts). Dubbing, i.e., the lip-synchronous translation and revoicing of speech adds to this constraints about the close matching of phonetic and resulting visemic synchrony characteristics of source and target material. There is an inherent conflict between a translation's meaning preservation and 'dubbability' and the resulting trade-off can be controlled by weighing the synchrony constraints. We introduce our work, which to the best of our knowledge is the first of its kind, on integrating synchrony constraints into the machine translation paradigm. We present first results for the integration of synchrony constraints into encoder decoder-based neural machine translation and show that considerably more 'dubbable' translations can be achieved with only a small impact on BLEU score, and dubbability improves more steeply than BLEU degrades.}, language = {en} } @inproceedings{HusseinMeyerSickendiekBaumann, author = {Hussein, Hussein and Meyer-Sickendiek, Burkhard and Baumann, Timo}, title = {Identification of Concrete Poetry within a Modern-Poetry Corpus using Neural Networks}, series = {Proceedings of the Quantitative Approaches to Versification Conference}, booktitle = {Proceedings of the Quantitative Approaches to Versification Conference}, address = {Prague, Czech Republic}, pages = {95 -- 104}, abstract = {This work aims to discern the poetics of concrete poetry by using a corpus-based classification focusing on the two most important techniques used within concrete poetry: semantic decomposition and syntactic permutation. We demonstrate how to identify concrete poetry in modern and postmodern free verse. A class contrasting to concrete poetry is defined on the basis of poems with complete and correct sentences. We used the data from lyrikline, which contain both the written as well as the spoken form of poems as read by the original author. We explored two approaches for the identification of concrete poetry. The first is based on the definition of concrete poetry in literary theory by the extraction of various types of features derived from a parser, such as verb, noun, comma, sentence ending, conjunction, and asemantic material. The second is a neural network-based approach, which is theoretically less informed by human insight, as it does not have access to features established by scholars. This approach used the following inputs: textual information and the spoken recitation of poetic lines as well as information about pauses between lines. The results based on the neural network are more accurate than the feature-based approach. The best results, calculated by the weighted F-measure, for the classification of concrete poetry vis-{\`a}-vis the contrasting class is 0.96}, language = {en} } @incollection{Baumann, author = {Baumann, Timo}, title = {Ranking and Comparing Speakers Based on Crowdsourced Pairwise Listener Ratings}, series = {Voice attractiveness: Studies on Sexy, Likable, and Charismatic Speakers}, booktitle = {Voice attractiveness: Studies on Sexy, Likable, and Charismatic Speakers}, editor = {Weiss, Benjamin and Trouvain, J{\"u}rgen and Barkat-Defradas, M{\´e}lissa and Ohala, John J.}, publisher = {Springer}, address = {Singapore}, isbn = {978-981-15-6626-4}, doi = {10.1007/978-981-15-6627-1_14}, pages = {263 -- 279}, abstract = {Speech quality and likability is a multi-faceted phenomenon consisting of a combination of perceptory features that cannot easily be computed nor weighed automatically. Yet, it is often easy to decide which of two voices one likes better, even though it would be hard to describe why, or to name the underlying basic perceptory features. Although likability is inherently subjective and individual preferences differ, generalizations are useful and there is often a broad intersubjective consensus about whether one speaker is more likeable than another. We present a methodology to efficiently create a likability ranking for many speakers from crowdsourced pairwise likability ratings which focuses manual rating effort on pairs of similar quality using an active sampling technique. Using this methodology, we collected pairwise likability ratings for many speakers (>220) from many raters (>160). We analyze listener preferences by correlating the resulting ranking with various acoustic and prosodic features. We also present a neural network that is able to model the complexity of listener preferences and the underlying temporal evolution of features. The recurrent neural network achieves remarkably high performance in estimating the pairwise decisions and an ablation study points toward the criticality of modeling temporal aspects in speech quality assessment.}, language = {en} }