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Evaluation of Tacotron Based Synthesizers for Spanish and Basque
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In: Applied Sciences; Volume 12; Issue 3; Pages: 1686 (2022)
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CCG Supertagging as Top-down Tree Generation
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In: Proceedings of the Society for Computation in Linguistics (2021)
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Vowel Harmony Viewed as Error-Correcting Code
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In: Proceedings of the Society for Computation in Linguistics (2021)
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Generating Adversarial Examples for Topic-dependent Argument Classification
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In: COMMA 2020 - 8th International Conference on Computational Models of Argument ; https://hal.archives-ouvertes.fr/hal-02933266 ; COMMA 2020 - 8th International Conference on Computational Models of Argument, Sep 2020, Perugia, Italy (2020)
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| 7 |
Automatic word count estimation from daylong child-centered recordings in various language environments using language-independent syllabification of speech
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| 8 |
Complexity of Stability
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In: Leibniz International Proceedings in Informatics, 181 ; 31st International Symposium on Algorithms and Computation (ISAAC 2020) (2020)
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| 9 |
NAT: Noise-Aware Training for Robust Neural Sequence Labeling
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In: Fraunhofer IAIS (2020)
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VoiceHome-2, an extended corpus for multichannel speech processing in real homes
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In: ISSN: 0167-6393 ; EISSN: 1872-7182 ; Speech Communication ; https://hal.inria.fr/hal-01923108 ; Speech Communication, Elsevier : North-Holland, 2019, 106, pp.68-78. ⟨10.1016/j.specom.2018.11.002⟩ (2019)
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| 11 |
Towards Interpretability and Robustness of Machine Learning Models
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Chen, Jianbo. - : eScholarship, University of California, 2019
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| 12 |
Assessing the Robustness of Conversational Agents using Paraphrases
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| 13 |
Robust speech recognition for german and dialectal broadcast programmes
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In: Fraunhofer IAIS (2018)
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Abstract:
Audio mining systems automatically analyse large amounts of heterogeneous media files such as television and radio programmes so that the analysed audio content can be efficiently searched for spoken words. Typically audio mining systems such as the Fraunhofer IAIS audio mining system consist of several modules to structure and analyse the data. The most important module is the large vocabulary continuous speech recognition (LVCSR) module, which is responsible to transform the audio signal into written text. Because of the tremendous developments in the field of speech recognition and to provide the customers with a high-performance audio mining system, the LVCSR module has to be trained and updated regularly by using the latest state-of-the-art algorithms provided by the research community and also by employing large amounts of training data. Today speech recognition systems usually perform very well in clean conditions, however when noise, reverberation or dialectal speakers are present, the performance of these systems degrade considerably. In broadcast media typically a large number of different speakers with high variability are present, like anchormen, interviewers, interviewees, speaking colloquial or planned speech, with or without dialect, or even with voice-overs. Especially in regional programmes of public broadcast, a considerable fraction of the speakers speak with an accent or a dialect. Also, a large amount of different background noises appears in the data, like background speech, or background music. Post-processing algorithms like compression, expansion, and stereo effect processing, which are generously used in broadcast media, further manipulate the audio data. All these issues make speech recognition in the broadcast domain a challenging task. This thesis focuses on the development and the optimisation of the German broadcast LVCSR system, which is part of the Fraunhofer IAIS audio mining system, over the course of several years, dealing with robustness related problems that arise for German broadcast media and also dealing with the requirements for the employment of the ASR system in a productive audiomining system for the industrial use including stability, decoding time and memory consumption. We approach the following three problems: the continuous development and optimisation of the German broadcast LVCSR system over a long period, rapidly finding the optimal ASR decoder parameters automatically and dealing with German dialects in the German broadcast LVCSR system. To guarantee superb performance over long periods of time, we regularly re-train the system using the latest algorithms and system architectures that became available by the research community, and evaluate the performance of the algorithms on German broadcast speech. We also drastically increase the training data by annotating a large and novel German broadcast speech corpus, which is unique in Germany. After training an automatic speech recognition (ASR) system, a speech recognition decoder is responsible to decode the most likely text hypothesis for a certain audio signal given the ASR model. Typically the ASR decoder comes with a large number of h yperparameters, which are usually set to default values or manually optimised. These parameters are often far from the optimum in terms of accuracy and decoding speed. State-of-the-art decoder parameter optimisation algorithms take a long time to converge. Hence, we approach the automatic decoder parameter optimisation in the context of German broadcast speech recognition in this thesis for both unconstrained and constrained (in terms of decoding speed) decoding, by introducing and extending an optimisation algorithm that has not been used for the task of speech recognitinon before to ASR decoder parameter optimisation. Germany has a large variety of dialects that are also often present in broadcast media especially in regional programmes. Dialectal speakers cause severely degraded perfor mance of the speech recognition system due to the mismatch in phonetics and grammar. In this thesis, we approach the large variety of German dialects by introducing a dialect identification system to infer the dialect of the speaker in order to use adapted dialectal speech recognition models to retrieve the spoken text. To train the dialect identification system, a novel database was collected and annotated. By approaching the three issues we arrive at an audio mining system that includes a high-performance speech recognition system, which is able to cope with dialectal speakers and with optimal decoder parameters that can be inferred quickly.
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Keyword:
Deep neural networks; dialectal robustness; gradient-free decoder parameter optimisation; robust speech recognition
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URL: http://publica.fraunhofer.de/documents/N-520009.html
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| 14 |
Learning Spatial-Semantic Context with Fully Convolutional Recurrent Network for Online Handwritten Chinese Text Recognition
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| 15 |
Lightweight Spoken Utterance Classification with CFG, tf-idf and Dynamic Programming
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In: ISBN: 978-3-319-68455-0 ; Statistical Language and Speech Processing (SLSP) pp. 143-154 (2017)
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A French corpus for distant-microphone speech processing in real homes
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In: Interspeech 2016 ; https://hal.inria.fr/hal-01343060 ; Interspeech 2016, Sep 2016, San Francisco, United States (2016)
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Reconnaissance automatique de gestes manuels en langue des signes
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In: RFIA 2016 ; RFIA'16: Le vingtième congrès national sur la Reconnaissance des Formes et l'Intelligence Artificielle ; https://hal.archives-ouvertes.fr/hal-01332141 ; RFIA'16: Le vingtième congrès national sur la Reconnaissance des Formes et l'Intelligence Artificielle , Jun 2016, Clermont-Ferrand, France (2016)
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| 18 |
Investigation of Back-off Based Interpolation Between Recurrent Neural Network and N-gram Language Models (Author's Manuscript)
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Lexicographic α-robustness: an application to the 1-median problem
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