activity
20182022
most citedAnonymizing Speech with Generative Adversarial Networks to Preserve Speaker Privacy

3 citations · 6 across the 6 of their papers we have counts for

collaborators

10 papers

cs.SD20223 cited

Anonymizing Speech with Generative Adversarial Networks to Preserve Speaker Privacy

Sarina Meyer, Pascal Tilli, Pavel Denisov +3

In order to protect the privacy of speech data, speaker anonymization aims for hiding the identity of a speaker by changing the voice in speech recordings. This typically comes wit…

cs.CL20212 cited

Investigations on Speech Recognition Systems for Low-Resource Dialectal Arabic-English Code-Switching Speech

Injy Hamed, Pavel Denisov, Chia-Yu Li +3

Code-switching (CS), defined as the mixing of languages in conversations, has become a worldwide phenomenon. The prevalence of CS has been recently met with a growing demand and in…

cs.CL2021

IMS' Systems for the IWSLT 2021 Low-Resource Speech Translation Task

Pavel Denisov, Manuel Mager, Ngoc Thang Vu

This paper describes the submission to the IWSLT 2021 Low-Resource Speech Translation Shared Task by IMS team. We utilize state-of-the-art models combined with several data augment…

eess.AS2020

Integration of speech separation, diarization, and recognition for multi-speaker meetings: System description, comparison, and analysis

Desh Raj, Pavel Denisov, Zhuo Chen +11

Multi-speaker speech recognition of unsegmented recordings has diverse applications such as meeting transcription and automatic subtitle generation. With technical advances in syst…

eess.AS2020

Pretrained Semantic Speech Embeddings for End-to-End Spoken Language Understanding via Cross-Modal Teacher-Student Learning

Pavel Denisov, Ngoc Thang Vu

Spoken language understanding is typically based on pipeline architectures including speech recognition and natural language understanding steps. These components are optimized ind…

cs.CL20201 cited

ADVISER: A Toolkit for Developing Multi-modal, Multi-domain and Socially-engaged Conversational Agents

Chia-Yu Li, Daniel Ortega, Dirk Väth +9

We present ADVISER - an open-source, multi-domain dialog system toolkit that enables the development of multi-modal (incorporating speech, text and vision), socially-engaged (e.g.…