François Portet


2020

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Corpus Generation for Voice Command in Smart Home and the Effect of Speech Synthesis on End-to-End SLU
Thierry Desot | François Portet | Michel Vacher
Proceedings of the 12th Language Resources and Evaluation Conference

Massive amounts of annotated data greatly contributed to the advance of the machine learning field. However such large data sets are often unavailable for novel tasks performed in realistic environments such as smart homes. In this domain, semantically annotated large voice command corpora for Spoken Language Understanding (SLU) are scarce, especially for non-English languages. We present the automatic generation process of a synthetic semantically-annotated corpus of French commands for smart-home to train pipeline and End-to-End (E2E) SLU models. SLU is typically performed through Automatic Speech Recognition (ASR) and Natural Language Understanding (NLU) in a pipeline. Since errors at the ASR stage reduce the NLU performance, an alternative approach is End-to-End (E2E) SLU to jointly perform ASR and NLU. To that end, the artificial corpus was fed to a text-to-speech (TTS) system to generate synthetic speech data. All models were evaluated on voice commands acquired in a real smart home. We show that artificial data can be combined with real data within the same training set or used as a stand-alone training corpus. The synthetic speech quality was assessedby comparing it to real data using dynamic time warping (DTW).

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Seq2SeqPy: A Lightweight and Customizable Toolkit for Neural Sequence-to-Sequence Modeling
Raheel Qader | François Portet | Cyril Labbe
Proceedings of the 12th Language Resources and Evaluation Conference

We present Seq2SeqPy a lightweight toolkit for sequence-to-sequence modeling that prioritizes simplicity and ability to customize the standard architectures easily. The toolkit supports several known architectures such as Recurrent Neural Networks, Pointer Generator Networks, and transformer model. We evaluate the toolkit on two datasets and we show that the toolkit performs similarly or even better than a very widely used sequence-to-sequence toolkit.

2019

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Fine-Grained Control of Sentence Segmentation and Entity Positioning in Neural NLG
Kritika Mehta | Raheel Qader | Cyril Labbe | François Portet
Proceedings of the 1st Workshop on Discourse Structure in Neural NLG

The move from pipeline Natural Language Generation (NLG) approaches to neural end-to-end approaches led to a loss of control in sentence planning operations owing to the conflation of intermediary micro-planning stages into a single model. Such control is highly necessary when the text should be tailored to respect some constraints such as which entity to be mentioned first, the entity position, the complexity of sentences, etc. In this paper, we introduce fine-grained control of sentence planning in neural data-to-text generation models at two levels - realization of input entities in desired sentences and realization of the input entities in the desired position among individual sentences. We show that by augmenting the input with explicit position identifiers, the neural model can achieve a great control over the output structure while keeping the naturalness of the generated text intact. Since sentence level metrics are not entirely suitable to evaluate this task, we used a metric specific to our task that accounts for the model’s ability to achieve control. The results demonstrate that the position identifiers do constraint the neural model to respect the intended output structure which can be useful in a variety of domains that require the generated text to be in a certain structure.

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Semi-Supervised Neural Text Generation by Joint Learning of Natural Language Generation and Natural Language Understanding Models
Raheel Qader | François Portet | Cyril Labbé
Proceedings of the 12th International Conference on Natural Language Generation

In Natural Language Generation (NLG), End-to-End (E2E) systems trained through deep learning have recently gained a strong interest. Such deep models need a large amount of carefully annotated data to reach satisfactory performance. However, acquiring such datasets for every new NLG application is a tedious and time-consuming task. In this paper, we propose a semi-supervised deep learning scheme that can learn from non-annotated data and annotated data when available. It uses a NLG and a Natural Language Understanding (NLU) sequence-to-sequence models which are learned jointly to compensate for the lack of annotation. Experiments on two benchmark datasets show that, with limited amount of annotated data, the method can achieve very competitive results while not using any pre-processing or re-scoring tricks. These findings open the way to the exploitation of non-annotated datasets which is the current bottleneck for the E2E NLG system development to new applications.

2018

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Generation of Company descriptions using concept-to-text and text-to-text deep models: dataset collection and systems evaluation
Raheel Qader | Khoder Jneid | François Portet | Cyril Labbé
Proceedings of the 11th International Conference on Natural Language Generation

In this paper we study the performance of several state-of-the-art sequence-to-sequence models applied to generation of short company descriptions. The models are evaluated on a newly created and publicly available company dataset that has been collected from Wikipedia. The dataset consists of around 51K company descriptions that can be used for both concept-to-text and text-to-text generation tasks. Automatic metrics and human evaluation scores computed on the generated company descriptions show promising results despite the difficulty of the task as the dataset (like most available datasets) has not been originally designed for machine learning. In addition, we perform correlation analysis between automatic metrics and human evaluations and show that certain automatic metrics are more correlated to human judgments.

2016

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The CIRDO Corpus: Comprehensive Audio/Video Database of Domestic Falls of Elderly People
Michel Vacher | Saïda Bouakaz | Marc-Eric Bobillier Chaumon | Frédéric Aman | R. A. Khan | Slima Bekkadja | François Portet | Erwan Guillou | Solange Rossato | Benjamin Lecouteux
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Ambient Assisted Living aims at enhancing the quality of life of older and disabled people at home thanks to Smart Homes. In particular, regarding elderly living alone at home, the detection of distress situation after a fall is very important to reassure this kind of population. However, many studies do not include tests in real settings, because data collection in this domain is very expensive and challenging and because of the few available data sets. The C IRDO corpus is a dataset recorded in realistic conditions in D OMUS , a fully equipped Smart Home with microphones and home automation sensors, in which participants performed scenarios including real falls on a carpet and calls for help. These scenarios were elaborated thanks to a field study involving elderly persons. Experiments related in a first part to distress detection in real-time using audio and speech analysis and in a second part to fall detection using video analysis are presented. Results show the difficulty of the task. The database can be used as standardized database by researchers to evaluate and compare their systems for elderly person’s assistance.

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CirdoX: an on/off-line multisource speech and sound analysis software
Frédéric Aman | Michel Vacher | François Portet | William Duclot | Benjamin Lecouteux
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Vocal User Interfaces in domestic environments recently gained interest in the speech processing community. This interest is due to the opportunity of using it in the framework of Ambient Assisted Living both for home automation (vocal command) and for call for help in case of distress situations, i.e. after a fall. C IRDO X, which is a modular software, is able to analyse online the audio environment in a home, to extract the uttered sentences and then to process them thanks to an ASR module. Moreover, this system perfoms non-speech audio event classification; in this case, specific models must be trained. The software is designed to be modular and to process on-line the audio multichannel stream. Some exemples of studies in which C IRDO X was involved are described. They were operated in real environment, namely a Living lab environment.

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Acquisition et reconnaissance automatique d’expressions et d’appels vocaux dans un habitat. (Acquisition and recognition of expressions and vocal calls in a smart home)
Michel Vacher | Benjamin Lecouteux | Frédéric Aman | François Portet | Solange Rossato
Actes de la conférence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP

Cet article présente un système capable de reconnaître les appels à l’aide de personnes âgées vivant à domicile afin de leur fournir une assistance. Le système utilise une technologie de Reconnaissance Automatique de la Parole (RAP) qui doit fonctionner en conditions de parole distante et avec de la parole expressive. Pour garantir l’intimité, le système s’exécute localement et ne reconnaît que des phrases prédéfinies. Le système a été évalué par 17 participants jouant des scénarios incluant des chutes dans un Living lab reproduisant un salon. Le taux d’erreur de détection obtenu, 29%, est encourageant et souligne les défis à surmonter pour cette tâche.

2015

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Proceedings of the 15th European Workshop on Natural Language Generation (ENLG)
Anya Belz | Albert Gatt | François Portet | Matthew Purver
Proceedings of the 15th European Workshop on Natural Language Generation (ENLG)

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Generating Récit from Sensor Data: Evaluation of a Task Model for Story Planning and Preliminary Experiments with GPS Data
Belén A. Baez Miranda | Sybille Caffiau | Catherine Garbay | François Portet
Proceedings of the 15th European Workshop on Natural Language Generation (ENLG)

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Proceedings of SLPAT 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies
Jan Alexandersson | Ercan Altinsoy | Heidi Christensen | Peter Ljunglöf | François Portet | Frank Rudzicz
Proceedings of SLPAT 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies

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Recognition of Distress Calls in Distant Speech Setting: a Preliminary Experiment in a Smart Home
Michel Vacher | Benjamin Lecouteux | Frédéric Aman | Solange Rossato | François Portet
Proceedings of SLPAT 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies

2014

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The Sweet-Home speech and multimodal corpus for home automation interaction
Michel Vacher | Benjamin Lecouteux | Pedro Chahuara | François Portet | Brigitte Meillon | Nicolas Bonnefond
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Ambient Assisted Living aims at enhancing the quality of life of older and disabled people at home thanks to Smart Homes and Home Automation. However, many studies do not include tests in real settings, because data collection in this domain is very expensive and challenging and because of the few available data sets. The S WEET-H OME multimodal corpus is a dataset recorded in realistic conditions in D OMUS, a fully equipped Smart Home with microphones and home automation sensors, in which participants performed Activities of Daily living (ADL). This corpus is made of a multimodal subset, a French home automation speech subset recorded in Distant Speech conditions, and two interaction subsets, the first one being recorded by 16 persons without disabilities and the second one by 6 seniors and 5 visually impaired people. This corpus was used in studies related to ADL recognition, context aware interaction and distant speech recognition applied to home automation controled through voice.

2013

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Proceedings of the Fourth Workshop on Speech and Language Processing for Assistive Technologies
Jan Alexandersson | Peter Ljunglöf | Kathleen F. McCoy | François Portet | Brian Roark | Frank Rudzicz | Michel Vacher
Proceedings of the Fourth Workshop on Speech and Language Processing for Assistive Technologies

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Analyzing the Performance of Automatic Speech Recognition for Ageing Voice: Does it Correlate with Dependency Level?
Frédéric Aman | Michel Vacher | Solange Rossato | François Portet
Proceedings of the Fourth Workshop on Speech and Language Processing for Assistive Technologies

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Experimental Evaluation of Speech Recognition Technologies for Voice-based Home Automation Control in a Smart Home
Michel Vacher | Benjamin Lecouteux | Dan Istrate | Thierry Joubert | François Portet | Mohamed Sehili | Pedro Chahuara
Proceedings of the Fourth Workshop on Speech and Language Processing for Assistive Technologies

2012

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Reconnaissance automatique de la parole distante dans un habitat intelligent : méthodes multi-sources en conditions réalistes (Distant Speech Recognition in a Smart Home : Comparison of Several Multisource ASRs in Realistic Conditions) [in French]
Benjamin Lecouteux | Michel Vacher | François Portet
Proceedings of the Joint Conference JEP-TALN-RECITAL 2012, volume 1: JEP

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Etude de la performance des modèles acoustiques pour des voix de personnes âgées en vue de l’adaptation des systèmes de RAP (Assessment of the acoustic models performance in the ageing voice case for ASR system adaptation) [in French]
Frédéric Aman | Michel Vacher | Solange Rossato | Remus Dugheanu | François Portet | Juline le Grand | Yuko Sasa
Proceedings of the Joint Conference JEP-TALN-RECITAL 2012, volume 1: JEP

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JEP-TALN-RECITAL 2012, Workshop ILADI 2012: Interactions Langagières pour personnes Agées Dans les habitats Intelligents (ILADI 2012: Language Interaction for Elderly in Smart Homes)
François Portet | Michel Vacher | Gilles Sérasset
JEP-TALN-RECITAL 2012, Workshop ILADI 2012: Interactions Langagières pour personnes Agées Dans les habitats Intelligents (ILADI 2012: Language Interaction for Elderly in Smart Homes)

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Les technologies de la parole et du TALN pour l’assistance à domicile des personnes âgées : un rapide tour d’horizon (Quick tour of NLP and speech technologies for ambient assisted living) [in French]
François Portet | Michel Vacher | Solange Rossato
JEP-TALN-RECITAL 2012, Workshop ILADI 2012: Interactions Langagières pour personnes Agées Dans les habitats Intelligents (ILADI 2012: Language Interaction for Elderly in Smart Homes)

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Reconnaissance d’ordres domotiques en conditions bruitées pour l’assistance à domicile (Recognition of Voice Commands by Multisource ASR and Noise Cancellation in a Smart Home Environment) [in French]
Benjamin Lecouteux | Michel Vacher | François Portet
JEP-TALN-RECITAL 2012, Workshop ILADI 2012: Interactions Langagières pour personnes Agées Dans les habitats Intelligents (ILADI 2012: Language Interaction for Elderly in Smart Homes)

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Contribution à l’étude de la variabilité de la voix des personnes âgées en reconnaissance automatique de la parole (Contribution to the study of elderly people’s voice variability in automatic speech recognition) [in French]
Frédéric Aman | Michel Vacher | Solange Rossato | François Portet
JEP-TALN-RECITAL 2012, Workshop ILADI 2012: Interactions Langagières pour personnes Agées Dans les habitats Intelligents (ILADI 2012: Language Interaction for Elderly in Smart Homes)

2011

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If it may have happened before, it happened, but not necessarily before
Albert Gatt | François Portet
Proceedings of the 13th European Workshop on Natural Language Generation

2010

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Textual Properties and Task-based Evaluation: Investigating the Role of Surface Properties, Structure and Content
Albert Gatt | François Portet
Proceedings of the 6th International Natural Language Generation Conference

2009

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Text Content and Task Performance in the Evaluation of a Natural Language Generation System
Albert Gatt | François Portet
Proceedings of the International Conference RANLP-2009

2008

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The Importance of Narrative and Other Lessons from an Evaluation of an NLG System that Summarises Clinical Data
Ehud Reiter | Albert Gatt | François Portet | Marian van der Meulen
Proceedings of the Fifth International Natural Language Generation Conference