Search Results for author: Petr Motlicek

Found 43 papers, 18 papers with code

IDIAP Submission@LT-EDI-ACL2022: Detecting Signs of Depression from Social Media Text

no code implementations LTEDI (ACL) 2022 Muskaan Singh, Petr Motlicek

Given social media postings in English, the submitted system classify the signs of depression into three labels, namely “not depressed,” “moderately depressed,” and “severely depressed.” Our best model is ranked 3^{rd} position with 0. 54% accuracy .

IDIAP Submission@LT-EDI-ACL2022 : Hope Speech Detection for Equality, Diversity and Inclusion

1 code implementation LTEDI (ACL) 2022 Muskaan Singh, Petr Motlicek

It reflects the belief to achieve an objective, discovers a new path, or become motivated to formulate pathways. In this paper we classify given a social media post, hope speech or not hope speech, using ensembled voting of BERT, ERNIE 2. 0 and RoBERTa for English language with 0. 54 macro F1-score (2^{st} rank).

Hope Speech Detection

Investigating Cross-lingual Multi-level Adaptive Networks: The Importance of the Correlation of Source and Target Languages

no code implementations IWSLT 2016 Alexandros Lazaridis, Ivan Himawan, Petr Motlicek, Iosif Mporas, Philip N. Garner

We experiment with three different scenarios using, i) French, as a source language uncorrelated to the target language, ii) Ukrainian, as a source language correlated to the target one and finally iii) English as a source language uncorrelated to the target language using a relatively large amount of data in respect to the other two scenarios.

Hierarchical Multi-task learning framework for Isometric-Speech Language Translation

1 code implementation IWSLT (ACL) 2022 Aakash Bhatnagar, Nidhir Bhavsar, Muskaan Singh, Petr Motlicek

In this paper, we propose a hierarchical approach to generate isometric translation on MUST-C dataset, we achieve a BERTscore of 0. 85, a length ratio of 1. 087, a BLEU score of 42. 3, and a length range of 51. 03%.

Machine Translation Multi-Task Learning +1

NLPHut’s Participation at WAT2021

no code implementations ACL (WAT) 2021 Shantipriya Parida, Subhadarshi Panda, Ketan Kotwal, Amulya Ratna Dash, Satya Ranjan Dash, Yashvardhan Sharma, Petr Motlicek, Ondřej Bojar

Our submission tops in English→Malayalam Multimodal translation task (text-only translation, and Malayalam caption), and ranks second-best in English→Hindi Multimodal translation task (text-only translation, and Hindi caption).

Image Captioning Translation

BertAA : BERT fine-tuning for Authorship Attribution

no code implementations ICON 2020 Maël Fabien, Esau Villatoro-Tello, Petr Motlicek, Shantipriya Parida

Identifying the author of a given text can be useful in historical literature, plagiarism detection, or police investigations.

Feature Engineering Language Modelling

Node-weighted Graph Convolutional Network for Depression Detection in Transcribed Clinical Interviews

1 code implementation3 Jul 2023 Sergio Burdisso, Esaú Villatoro-Tello, Srikanth Madikeri, Petr Motlicek

We propose a simple approach for weighting self-connecting edges in a Graph Convolutional Network (GCN) and show its impact on depression detection from transcribed clinical interviews.

Depression Detection

HyperConformer: Multi-head HyperMixer for Efficient Speech Recognition

1 code implementation29 May 2023 Florian Mai, Juan Zuluaga-Gomez, Titouan Parcollet, Petr Motlicek

In particular, multi-head HyperConformer achieves comparable or higher recognition performance while being more efficient than Conformer in terms of inference speed, memory, parameter count, and available training data.

speech-recognition Speech Recognition

A Virtual Simulation-Pilot Agent for Training of Air Traffic Controllers

no code implementations16 Apr 2023 Juan Zuluaga-Gomez, Amrutha Prasad, Iuliia Nigmatulina, Petr Motlicek, Matthias Kleinert

The overall pipeline is composed of the following submodules: (i) automatic speech recognition (ASR) system that transforms audio into a sequence of words; (ii) high-level air traffic control (ATC) related entity parser that understands the transcribed voice communication; and (iii) a text-to-speech submodule that generates a spoken utterance that resembles a pilot based on the situation of the dialogue.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Speech and Natural Language Processing Technologies for Pseudo-Pilot Simulator

no code implementations14 Dec 2022 Amrutha Prasad, Juan Zuluaga-Gomez, Petr Motlicek, Saeed Sarfjoo, Iuliia Nigmatulina, Karel Vesely

The system understands the voice communications issued by the ATCo, and, in turn, it generates a spoken prompt that follows the pilot's phraseology to the initial communication.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Claim-Dissector: An Interpretable Fact-Checking System with Joint Re-ranking and Veracity Prediction

1 code implementation28 Jul 2022 Martin Fajcik, Petr Motlicek, Pavel Smrz

We propose to disentangle the per-evidence relevance probability and its contribution to the final veracity probability in an interpretable way -- the final veracity probability is proportional to a linear ensemble of per-evidence relevance probabilities.

Fact Checking Re-Ranking

How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications

2 code implementations31 Mar 2022 Juan Zuluaga-Gomez, Amrutha Prasad, Iuliia Nigmatulina, Saeed Sarfjoo, Petr Motlicek, Matthias Kleinert, Hartmut Helmke, Oliver Ohneiser, Qingran Zhan

Recent work on self-supervised pre-training focus on leveraging large-scale unlabeled speech data to build robust end-to-end (E2E) acoustic models (AM) that can be later fine-tuned on downstream tasks e. g., automatic speech recognition (ASR).

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

A two-step approach to leverage contextual data: speech recognition in air-traffic communications

no code implementations8 Feb 2022 Iuliia Nigmatulina, Juan Zuluaga-Gomez, Amrutha Prasad, Seyyed Saeed Sarfjoo, Petr Motlicek

Automatic Speech Recognition (ASR), as the assistance of speech communication between pilots and air-traffic controllers, can significantly reduce the complexity of the task and increase the reliability of transmitted information.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +5

BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications

2 code implementations12 Oct 2021 Juan Zuluaga-Gomez, Seyyed Saeed Sarfjoo, Amrutha Prasad, Iuliia Nigmatulina, Petr Motlicek, Karel Ondrej, Oliver Ohneiser, Hartmut Helmke

We propose a system that combines SAD and a BERT model to perform speaker change detection and speaker role detection (SRD) by chunking ASR transcripts, i. e., SD with a defined number of speakers together with SRD.

Action Detection Activity Detection +7

Grammar Based Speaker Role Identification for Air Traffic Control Speech Recognition

no code implementations27 Aug 2021 Amrutha Prasad, Juan Zuluaga-Gomez, Petr Motlicek, Saeed Sarfjoo, Iuliia Nigmatulina, Oliver Ohneiser, Hartmut Helmke

In this work, we propose to (1) automatically segment the ATCO and pilot data based on an intuitive approach exploiting ASR transcripts and (2) subsequently consider an automatic recognition of ATCOs' and pilots' voice as two separate tasks.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

IEEE SLT 2021 Alpha-mini Speech Challenge: Open Datasets, Tracks, Rules and Baselines

1 code implementation4 Nov 2020 Yihui Fu, Zhuoyuan Yao, Weipeng He, Jian Wu, Xiong Wang, Zhanheng Yang, Shimin Zhang, Lei Xie, DongYan Huang, Hui Bu, Petr Motlicek, Jean-Marc Odobez

In this challenge, we open source a sizable speech, keyword, echo and noise corpus for promoting data-driven methods, particularly deep-learning approaches on KWS and SSL.

Sound Audio and Speech Processing

Pkwrap: a PyTorch Package for LF-MMI Training of Acoustic Models

1 code implementation7 Oct 2020 Srikanth Madikeri, Sibo Tong, Juan Zuluaga-Gomez, Apoorv Vyas, Petr Motlicek, Hervé Bourlard

We present a simple wrapper that is useful to train acoustic models in PyTorch using Kaldi's LF-MMI training framework.

Audio and Speech Processing Sound

Automatic Speech Recognition Benchmark for Air-Traffic Communications

3 code implementations18 Jun 2020 Juan Zuluaga-Gomez, Petr Motlicek, Qingran Zhan, Karel Vesely, Rudolf Braun

We demonstrate that the cross-accent flaws due to speakers' accents are minimized due to the amount of data, making the system feasible for ATC environments.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Idiap NMT System for WAT 2019 Multimodal Translation Task

no code implementations WS 2019 Shantipriya Parida, Ond{\v{r}}ej Bojar, Petr Motlicek

This paper describes the Idiap submission to WAT 2019 for the English-Hindi Multi-Modal Translation Task.

NMT Translation

Abstract Text Summarization: A Low Resource Challenge

no code implementations IJCNLP 2019 Shantipriya Parida, Petr Motlicek

We propose an iterative data augmentation approach which uses synthetic data along with the real summarization data for the German language.

Data Augmentation Text Summarization

Deep Neural Networks for Multiple Speaker Detection and Localization

1 code implementation30 Nov 2017 Weipeng He, Petr Motlicek, Jean-Marc Odobez

We propose to use neural networks for simultaneous detection and localization of multiple sound sources in human-robot interaction.

The DBOX Corpus Collection of Spoken Human-Human and Human-Machine Dialogues

no code implementations LREC 2014 Volha Petukhova, Martin Gropp, Dietrich Klakow, Gregor Eigner, Mario Topf, Stefan Srb, Petr Motlicek, Blaise Potard, John Dines, Olivier Deroo, Ronny Egeler, Uwe Meinz, Steffen Liersch, Anna Schmidt

We first start with human-human Wizard of Oz experiments to collect human-human data in order to model natural human dialogue behaviour, for better understanding of phenomena of human interactions and predicting interlocutors actions, and then replace the human Wizard by an increasingly advanced dialogue system, using evaluation data for system improvement.

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