Paul Deléglise

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This paper describes the three systems developed by the LIUM for the IWSLT 2011 evaluation campaign. We participated in three of the proposed tasks, namely the Automatic Speech Recognition task (ASR), the ASR system combination task (ASR_SC) and the Spoken Language Translation task (SLT), since these tasks are all related to speech translation. We present(More)
In this paper, we present improvements made to the TED-LIUM corpus we released in 2012. These enhancements fall into two categories. First, we describe how we filtered publicly available monolingual data and used it to estimate well-suited language models (LMs), using open-source tools. Then, we describe the process of selection we applied to new acoustic(More)
This paper presents the corpus developed by the LIUM for Automatic Speech Recognition (ASR), based on the TED Talks. This corpus was built during the IWSLT 2011 Evaluation Campaign, and is composed of 118 hours of speech with its accompanying automatically aligned transcripts. We describe the content of the corpus, how the data was collected and processed,(More)
This paper presents the system used by the LIUM to participate in ESTER, the french broadcast news evaluation campaign. This system is based on the CMU Sphinx 3.3 (fast) decoder. Some tools are presented which have been added on different steps of the Sphinx recognition process: segmentation, acoustic model adaptation, word-lattice rescoring. Several(More)
This paper deals with the integration of visual data in automatic speech recognition systems. We rst describe the framework of our research; the development of advanced multi-user multi-modal interfaces. Then we present audiovisual speech recognition problems in general, and the ones we are interested in, in particular. After a very brief discussion of(More)
This paper describes the new ASR system developed by the LIUM and analyzes the various origins of the significant drop of the word error rate observed in comparison to the previous LIUM ASR system. This study was made on the test data of the latest evaluation campaign of ASR systems on French broadcast news, called ESTER2 and organized in December 2008. For(More)
Among the various methods proposed to improve the accuracy and the robustness of automatic speech recognition (ASR), the use of additional knowledge sources is a successful one. In particular, a recent method proposes supplementing the acoustic information with visual data mostly derived from the speaker's lip shape. Perceptual studies support this approach(More)
This paper presents an automatic grapheme to phoneme conversion system that uses statistical machine translation techniques provided by the Moses Toolkit. The generated word pronunciations are employed in the dictionary of an automatic speech recognition system and evaluated using the ESTER 2 French broadcast news corpus. Grapheme to phoneme conversion(More)
Automatic speaker diarization generally produces a generic label such a spkr1 rather than the true identity of the speaker. Recently, two approaches based on lexical rules were proposed to extract the true identity of the speaker from the transcriptions of the audio recording without any a priori acoustic information: one uses n-gram, the other one uses(More)