Search Results for author:
Found 19 papers, 9 papers with code
The Llama 3 Herd of Models
This paper presents a new set of foundation models, called Llama 3.
Ranked #2 on
Multi-task Language Understanding
on MMLU
Discrete Flow Matching
Despite Flow Matching and diffusion models having emerged as powerful generative paradigms for continuous variables such as images and videos, their application to high-dimensional discrete data, such as language, is still limited.
Masked Audio Generation using a Single Non-Autoregressive Transformer
We introduce MAGNeT, a masked generative sequence modeling method that operates directly over several streams of audio tokens.
EXPRESSO: A Benchmark and Analysis of Discrete Expressive Speech Resynthesis
Recent work has shown that it is possible to resynthesize high-quality speech based, not on text, but on low bitrate discrete units that have been learned in a self-supervised fashion and can therefore capture expressive aspects of speech that are hard to transcribe (prosody, voice styles, non-verbal vocalization).
Simple and Controllable Music Generation
We tackle the task of conditional music generation.
Ranked #7 on
Text-to-Music Generation
on MusicCaps
Textually Pretrained Speech Language Models
In this work, we propose TWIST, a method for training SpeechLMs using a warm-start from a pretrained textual language models.
Ranked #4 on
Language Modelling
on SALMon
(using extra training data)
Augmentation Invariant Discrete Representation for Generative Spoken Language Modeling
This work focuses on improving the robustness of discrete input representations for generative spoken language modeling.
AudioGen: Textually Guided Audio Generation
Finally, we explore the ability of the proposed method to generate audio continuation conditionally and unconditionally.
Ranked #13 on
Audio Generation
on AudioCaps
A Systematic Comparison of Phonetic Aware Techniques for Speech Enhancement
By conducting a series of controlled experiments, we observe the influence of different phonetic content models as well as various feature-injection techniques on enhancement performance, considering both causal and non-causal models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Self-supervised Speaker Diarization
Specifically, the study focuses on generating high-quality neural speaker representations without any annotated data, as well as on estimating secondary hyperparameters of the model without annotations.
Correcting Mispronunciations in Speech using Spectrogram Inpainting
Grownup learners of a language tweak their speech to match the tutor reference.
Textless Speech Emotion Conversion using Decomposed & Discrete Representations
We decompose speech into discrete and disentangled learned representations, consisting of content units, F0, speaker, and emotion.
Textless Speech Emotion Conversion using Discrete and Decomposed Representations
We use a decomposition of the speech signal into discrete learned representations, consisting of phonetic-content units, prosodic features, speaker, and emotion.
Self-Supervised Contrastive Learning for Unsupervised Phoneme Segmentation
Results suggest that our approach surpasses the baseline models and reaches state-of-the-art performance on both data sets.
A causal view of compositional zero-shot recognition
This leads to consistent misclassification of samples from a new distribution, like new combinations of known components.
Phoneme Boundary Detection using Learnable Segmental Features
Phoneme boundary detection plays an essential first step for a variety of speech processing applications such as speaker diarization, speech science, keyword spotting, etc.
Hide and Speak: Towards Deep Neural Networks for Speech Steganography
Steganography is the science of hiding a secret message within an ordinary public message, which is referred to as Carrier.
Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples
Deep learning models have been successfully applied to malware detection.
Fooling End-to-end Speaker Verification by Adversarial Examples
We also present two black-box attacks: where the adversarial examples were generated with a system that was trained on YOHO, but the attack is on a system that was trained on NTIMIT; and when the adversarial examples were generated with a system that was trained on Mel-spectrum feature set, but the attack is on a system that was trained on MFCC.
