Search Results for author:
Found 17 papers, 11 papers with code
ASVspoof 5: Design, Collection and Validation of Resources for Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech
The database contains attacks generated with 32 different algorithms, also crowdsourced, and optimised to varying degrees using new surrogate detection models.
High-Resolution Speech Restoration with Latent Diffusion Model
Traditional speech enhancement methods often oversimplify the task of restoration by focusing on a single type of distortion.
Probing the Feasibility of Multilingual Speaker Anonymization
In speaker anonymization, speech recordings are modified in a way that the identity of the speaker remains hidden.
Meta Learning Text-to-Speech Synthesis in over 7000 Languages
In this work, we take on the challenging task of building a single text-to-speech synthesis system that is capable of generating speech in over 7000 languages, many of which lack sufficient data for traditional TTS development.
Controlling Emotion in Text-to-Speech with Natural Language Prompts
In recent years, prompting has quickly become one of the standard ways of steering the outputs of generative machine learning models, due to its intuitive use of natural language.
Controllable Generation of Artificial Speaker Embeddings through Discovery of Principal Directions
Customizing voice and speaking style in a speech synthesis system with intuitive and fine-grained controls is challenging, given that little data with appropriate labels is available.
The IMS Toucan System for the Blizzard Challenge 2023
For our contribution to the Blizzard Challenge 2023, we improved on the system we submitted to the Blizzard Challenge 2021.
Low-Resource Multilingual and Zero-Shot Multispeaker TTS
While neural methods for text-to-speech (TTS) have shown great advances in modeling multiple speakers, even in zero-shot settings, the amount of data needed for those approaches is generally not feasible for the vast majority of the world's over 6, 000 spoken languages.
Combining Contrastive and Non-Contrastive Losses for Fine-Tuning Pretrained Models in Speech Analysis
This pretrained model is then finetuned to a specific task.
Anonymizing Speech with Generative Adversarial Networks to Preserve Speaker Privacy
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.
PoeticTTS -- Controllable Poetry Reading for Literary Studies
Speech synthesis for poetry is challenging due to specific intonation patterns inherent to poetic speech.
Speaker Anonymization with Phonetic Intermediate Representations
In this work, we propose a speaker anonymization pipeline that leverages high quality automatic speech recognition and synthesis systems to generate speech conditioned on phonetic transcriptions and anonymized speaker embeddings.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Exact Prosody Cloning in Zero-Shot Multispeaker Text-to-Speech
The cloning of a speaker's voice using an untranscribed reference sample is one of the great advances of modern neural text-to-speech (TTS) methods.
Language-Agnostic Meta-Learning for Low-Resource Text-to-Speech with Articulatory Features
While neural text-to-speech systems perform remarkably well in high-resource scenarios, they cannot be applied to the majority of the over 6, 000 spoken languages in the world due to a lack of appropriate training data.
Meta-Learning for improving rare word recognition in end-to-end ASR
We propose a new method of generating meaningful embeddings for speech, changes to four commonly used meta learning approaches to enable them to perform keyword spotting in continuous signals and an approach of combining their outcomes into an end-to-end automatic speech recognition system to improve rare word recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
ADVISER: A Toolkit for Developing Multi-modal, Multi-domain and Socially-engaged Conversational Agents
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. emotion recognition, engagement level prediction and backchanneling) conversational agents.
Multiclass Text Classification on Unbalanced, Sparse and Noisy Data
This paper discusses methods to improve the performance of text classification on data that is difficult to classify due to a large number of unbalanced classes with noisy examples.
