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AM-FM DECOMPOSITION OF SPEECH SIGNAL: APPLICATIONS FOR SPEECH PRIVACY AND DIAGNOSIS
Publications associées (92)
Sparse Autoencoders for Speech Modeling and Recognition
Speech recognition-based applications upon the advancements in artificial intelligence play an essential role to transform most aspects of modern life. However, speech recognition in real-life conditions (e.g., in the presence of overlapping speech, varyin ...
EPFL2023Bertraffic: Bert-Based Joint Speaker Role And Speaker Change Detection For Air Traffic Control Communications
Petr Motlicek, Juan Pablo Zuluaga Gomez, Amrutha Prasad
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 ...
IEEE2022Automatic Call Sign Detection: Matching Air Surveillance Data with Air Traffic Spoken Communications
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 ...
MDPI2021ROXANNE 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 ASSOC2021Automatic Speech Recognition Benchmark for Air-Traffic Communications
Advances in Automatic Speech Recognition (ASR) over the last decade opened new areas of speech-based automation such as in Air-Traffic Control (ATC) environments. Currently, voice communication and Controller Pilot Data Link Communications are the only way ...
ISCA2020AM-FM DECOMPOSITION OF SPEECH SIGNAL: APPLICATIONS FOR SPEECH PRIVACY AND DIAGNOSIS
Petr Motlicek, Hynek Hermansky, Sriram Ganapathy, Amrutha Prasad
Although current trends in speech processing consider deep learning through data-driven technologies, many potential applications exhibit lack of training or development data. Therefore, considerably light signal processing techniques are still of interest ...
2019Understanding and Visualizing Raw Waveform-based CNNs
Sébastien Marcel, Hannah Muckenhirn
Modeling directly raw waveforms through neural networks for speech processing is gaining more and more attention. Despite its varied success, a question that remains is: what kind of information are such neural networks capturing or learning for different ...
2019End-to-End Acoustic Modeling using Convolutional Neural Networks for HMM-based Automatic Speech Recognition
Ronan Collobert, Dimitri Palaz
In hidden Markov model (HMM) based automatic speech recognition (ASR) system, modeling the statistical relationship between the acoustic speech signal and the HMM states that represent linguistically motivated subword units such as phonemes is a crucial st ...
ELSEVIER SCIENCE BV2019Trustworthy speaker recognition with minimal prior knowledge using neural networks
The performance of speaker recognition systems has considerably improved in the last decade. This is mainly due to the development of Gaussian mixture model-based systems and in particular to the use of i-vectors. These systems handle relatively well noise ...
EPFL2019Spectral Subspace Analysis for Automatic Assessment of Pathological Speech Intelligibility
Hervé Bourlard, Ina Kodrasi, Parvaneh Janbakhshi
Speech intelligibility is an important assessment criterion of the communicative performance of pathological speakers. To assist clinicians in their assessment, time- and cost-efficient automatic intelligibility measures offering a repeatable and reliable ...
2019