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Found 17 papers, 1 papers with code
Adapting Language Models When Training on Privacy-Transformed Data
In recent years, voice-controlled personal assistants have revolutionized the interaction with smart devices and mobile applications.
Evaluation of Speaker Anonymization on Emotional Speech
This paper studies the impact of the speaker anonymization baseline system of the VPC on emotional information present in speech utterances.
Are disentangled representations all you need to build speaker anonymization systems?
We propose enhancing the disentanglement by removing speaker information from the acoustic model using vector quantization.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Privacy-Preserving Speech Representation Learning using Vector Quantization
With the popularity of virtual assistants (e. g., Siri, Alexa), the use of speech recognition is now becoming more and more widespread. However, speech signals contain a lot of sensitive information, such as the speaker's identity, which raises privacy concerns. The presented experiments show that the representations extracted by the deep layers of speech recognition networks contain speaker information. This paper aims to produce an anonymous representation while preserving speech recognition performance. To this end, we propose to use vector quantization to constrain the representation space and induce the network to suppress the speaker identity. The choice of the quantization dictionary size allows to configure the trade-off between utility (speech recognition) and privacy (speaker identity concealment).
On the invertibility of a voice privacy system using embedding alignement
This paper explores various attack scenarios on a voice anonymization system using embeddings alignment techniques.
Evaluating X-vector-based Speaker Anonymization under White-box Assessment
In the scenario of the Voice Privacy challenge, anonymization is achieved by converting all utterances from a source speaker to match the same target identity; this identity being randomly selected.
A Study of F0 Modification for X-Vector Based Speech Pseudonymization Across Gender
Speech pseudonymization aims at altering a speech signal to map the identifiable personal characteristics of a given speaker to another identity.
Projet AMIS : r\'esum\'e et traduction automatique de vid\'eos (AMIS project : automatic summarization and translation of videos)
Un autre objectif du projet {\'e}tait aussi de comparer les opinions et sentiments exprim{\'e}s dans plusieurs vid{\'e}os comparables.
Adaptation de domaine non supervis\'ee pour la reconnaissance de la langue par r\'egularisation d'un r\'eseau de neurones (Unsupervised domain adaptation for language identification by regularization of a neural network)
Sur le corpus RATS, pour sept des huit canaux radio {\'e}tudi{\'e}s, l{'}approche permet, sans utiliser de donn{\'e}es annot{\'e}es du domaine cible, de surpasser la performance d{'}un syst{\`e}me entra{\^\i}n{\'e} de fa{\c{c}}on supervis{\'e}e avec des donn{\'e}es annot{\'e}es de ce domaine.
\'Etude comparative de corr\'elats prosodiques de marqueurs discursifs fran\ccais et anglais selon leur fonction pragmatique (Comparative study on prosodic correlates of discourse markers in French and English according to their pragmatic function)
L{'}analyse a {\'e}t{\'e} effectu{\'e}e sur plusieurs centaines d{'}occurrences de marqueurs discursifs extraits de corpus oraux fran{\c{c}}ais et anglais.
An enhanced automatic speech recognition system for Arabic
Despite all the classical techniques for Automatic Speech Recognition (ASR), which can be efficiently applied to Arabic speech recognition, it is essential to take into consideration the language specificities to improve the system performance.
The IFCASL Corpus of French and German Non-native and Native Read Speech
The IFCASL corpus is a French-German bilingual phonetic learner corpus designed, recorded and annotated in a project on individualized feedback in computer-assisted spoken language learning.
Designing a Bilingual Speech Corpus for French and German Language Learners: a Two-Step Process
We present the design of a corpus of native and non-native speech for the language pair French-German, with a special emphasis on phonetic and prosodic aspects.
