Real to H-Space Autoencoders for Theme Identification in Telephone Conversations

Autor: Xavier Bost, Titouan Parcollet, Renato De Mori, Mohamed Morchid, Georges Linarès

Publikováno v: IEEE/ACM Transactions on Audio, Speech and Language Processing
IEEE/ACM Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2020, 28, pp.198-210. ⟨10.1109/TASLP.2019.2950596⟩

International audience; Machine learning (ML) and deep learning with deep neural networks (DNN), have drastically improved the performances of modern systems on numerous spoken language understanding (SLU) related tasks. Since most of current researc

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::df2c58bb1f708dddbe068832ef3cc8f4
https://doi.org/10.1109/taslp.2019.2950596

End2End Acoustic to Semantic Transduction

Autor: Renato De Mori, Antoine Laurent, Sylvain Meignier, Antoine Caubrière, Valentin Pelloin, Nathalie Camelin, Yannick Estève

Publikováno v: ICASSP
ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Jun 2021, Toronto, ON, Canada. ⟨10.1109/ICASSP39728.2021.9413581⟩

In this paper, we propose a novel end-to-end sequence-to-sequence spoken language understanding model using an attention mechanism. It reliably selects contextual acoustic features in order to hypothesize semantic contents. An initial architecture ca

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a1d4edfd570189d628f12c702fc27fa6

Denoised Bottleneck Features From Deep Autoencoders for Telephone Conversation Analysis

Autor: Richard Dufour, Mohamed Morchid, Killian Janod, Georges Linarès, Renato De Mori

Publikováno v: IEEE/ACM Transactions on Audio, Speech and Language Processing
IEEE/ACM Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2017, 25 (9), pp.1809-1820. ⟨10.1109/TASLP.2017.2718843⟩

Automatic transcription of spoken documents is affected by automatic transcription errors that are especially frequent when speech is acquired in severe noisy conditions. Automatic speech recognition errors induce errors in the linguistic features us

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::002085d191f0f9dbca9d9c30fc5ef837
https://doi.org/10.1109/taslp.2017.2718843

Real to H-space Encoder for Speech Recognition

Autor: Georges Linarès, Mohamed Morchid, Renato De Mori, Titouan Parcollet

Publikováno v: INTERSPEECH 2019
INTERSPEECH 2019, Jun 2019, Graz, Austria
INTERSPEECH

Deep neural networks (DNNs) and more precisely recurrent neural networks (RNNs) are at the core of modern automatic speech recognition systems, due to their efficiency to process input sequences. Recently, it has been shown that different input repre

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5b861c082624b8ed15e3fdc9eba6f156
https://hal.archives-ouvertes.fr/hal-02158201/file/INTERSPEECH_2019___Projection_QLSTM-3.pdf

Multiple topic identification in telephone conversations

Autor: Renato De Mori, Marc El-Bèze, Xavier Bost

Publikováno v: Interspeech
Interspeech, Aug 2013, Lyon, France
INTERSPEECH

This paper deals with the automatic analysis of conversations between a customer and an agent in a call centre of a customer care service. The purpose of the analysis is to hypothesize themes about problems and complaints discussed in the conversatio

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::92412e3b2411744e987504bb92724bbb
http://arxiv.org/abs/1812.09321