Publications de Petr Motlicek | EPFL Graph Search

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

Petr Motlicek, Amrutha Prasad, Juan Pablo Zuluaga Gomez

In this paper we propose a novel virtual simulation-pilot engine for speeding up air traffic controller (ATCo) training by integrating different state-of-the-art artificial intelligence (AI)-based tools. The virtual simulation-pilot engine receives spoken ...

MDPI2023

How Does Pre-Trained Wav2Vec 2.0 Perform On Domain-Shifted Asr? An Extensive Benchmark On Air Traffic Control Communications

Petr Motlicek, Amrutha Prasad, Juan Pablo Zuluaga Gomez

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). Yet, few work ...

IEEE2022

Bertraffic: Bert-Based Joint Speaker Role And Speaker Change Detection For Air Traffic Control Communications

Petr Motlicek, Amrutha Prasad, Juan Pablo Zuluaga Gomez

Automatic speech recognition (ASR) allows transcribing the communications between air traffic controllers (ATCOs) and aircraft pilots. The transcriptions are used later to extract ATC named entities, e.g., aircraft callsigns. One common challenge is speech ...

IEEE2022

A Two-Step Approach To Leverage Contextual Data: Speech Recognition In Air-Traffic Communications

Petr Motlicek, Amrutha Prasad, Juan Pablo Zuluaga Gomez

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. ASR application can lead ...

IEEE2022

Multitask adaptation with Lattice-Free MMI for multi-genre speech recognition of low resource languages

Hervé Bourlard, Petr Motlicek

In this paper, we develop Automatic Speech Recognition (ASR) systems for multi-genre speech recognition of low-resource languages where training data is predominantly conversational speech but test data can be in one of the following genres: news broadcast ...

ISCA-INT SPEECH COMMUNICATION ASSOC2021

Late Fusion of the Available Lexicon and Raw Waveform-based Acoustic Modeling for Depression and Dementia Recognition

Mathew Magimai Doss, Petr Motlicek, Subrahmanya Pavankumar Dubagunta, Julian David Fritsch

Mental disorders, e.g. depression and dementia, are categorized as priority conditions according to the World Health Organization (WHO). When diagnosing, psychologists employ structured questionnaires/interviews, and different cognitive tests. Although acc ...

ISCA-INT SPEECH COMMUNICATION ASSOC2021

Neural Network Adaptation and Data Augmentation for Multi-Speaker Direction-of-Arrival Estimation

Jean-Marc Odobez, Petr Motlicek, Weipeng He

IEEE2021

Contextual Semi-Supervised Learning: An Approach To Leverage Air-Surveillance and Untranscribed ATC Data in ASR Systems

Petr Motlicek, Amrutha Prasad, Juan Pablo Zuluaga Gomez

Air traffic management and specifically air-traffic control (ATC) rely mostly on voice communications between Air Traffic Controllers (ATCos) and pilots. In most cases, these voice communications follow a well-defined grammar that could be leveraged in Aut ...

ISCA-INT SPEECH COMMUNICATION ASSOC2021

Automatic Call Sign Detection: Matching Air Surveillance Data with Air Traffic Spoken Communications

Petr Motlicek, Amrutha Prasad

Voice communication is the main channel to exchange information between pilots and Air-Traffic Controllers (ATCos). Recently, several projects have explored the employment of speech recognition technology to automatically extract spoken key information suc ...

MDPI2021

Multi-task Neural Network for Robust Multiple Speaker Embedding Extraction

Jean-Marc Odobez, Petr Motlicek, Weipeng He

This paper introduces a novel approach for extracting speaker embeddings from audio mixtures of multiple overlapping voices. This approach is based on a multi-task neural network. The network first extracts a latent feature for each direction. This feature ...

ISCA-INT SPEECH COMMUNICATION ASSOC2021

A Comparison of Methods for OOV-Word Recognition on a New Public Dataset

Petr Motlicek

A common problem for automatic speech recognition systems is how to recognize words that they did not see during training. Currently there is no established method of evaluating different techniques for tackling this problem. We propose using the CommonVoi ...

IEEE Signal Processing Society2021

Boosting of contextual information in ASR for air-traffic call-sign recognition

Petr Motlicek, Juan Pablo Zuluaga Gomez

Contextual adaptation of ASR can be very beneficial for multi-accent and often noisy Air-Traffic Control (ATC) speech. Our focus is call-sign recognition, which can be used to track conversations of ATC operators with individual airplanes. We developed a t ...

ISCA-INT SPEECH COMMUNICATION ASSOC2021

ROXANNE Research Platform: Automate criminal investigations

Petr Motlicek, Maël Fabien, Aravind Krishnan

Criminal investigations require manual intervention of several investigators and translators. However, the amount and the diversity of the data collected raises many challenges, and cross-border investigations against organized crime can quickly impossible ...

ISCA-INT SPEECH COMMUNICATION ASSOC2021

OdiEnCorp 2.0: Odia-English Parallel Corpus for Machine Translation

Petr Motlicek

The preparation of parallel corpora is a challenging task, particularly for languages that suffer from under-representation in the digital world. In a multi-lingual country like India, the need for such parallel corpora is stringent for several low-resourc ...

European Language Resources Association (ELRA)2020

Idiap Submission to Swiss-German Language Detection Shared Task

Petr Motlicek

Language detection is a key part of the NLP pipeline for text processing. The task of automatically detecting languages belonging to disjoint groups is relatively easy. It is considerably challenging to detect languages that have similar origins or dialect ...

CEUR Workshop Proceedings2020

Classification and Regression of Cognitive and Motivational Style from Text

Petr Motlicek

In this paper, we describe the participation of the Idiap Research Institute at GermEval 2020 shared task on the Classification and Regression of Cognitive and Motivational style from Text, specifically on subtask 2, Classification of the Operant Motive Te ...

2020

Inferring Highly-dense Representations for Clustering Broadcast Media Content

Petr Motlicek

PBML2020

Incremental Semi-Supervised Learning for Multi-Genre Speech Recognition

Hervé Bourlard, Petr Motlicek, Subhadeep Dey

2020

Detection of Similar Languages and Dialects Using Deep Supervised Autoencoders

Petr Motlicek, Maël Fabien

ACL2020

Idiap and UAM Participation at MEX-A3T Evaluation Campaign

Petr Motlicek

This paper describes our participation in the shared evaluation campaign of MexA3T 2020. Our main goal wasto evaluate a Supervised Autoencoder (SAE) learning algorithm in text classification tasks. For our experiments,we used three different sets of featur ...

CEUR Workshop Proceedings2020