19 citations · 29 across the 27 of their papers we have counts for
14 papers · 1 filter
ProPS: Prompted Profile Synthesis for Natural Language-Conditioned Speaker Embedding Distributions
Thomas Thebaud, Junhyeok Lee, Laureano Moro-Velazquez +2
Speaker embeddings, or x-vectors, are widely used to represent speaker identity and speaker-related attributes, but existing embedding extractors are typically descriptive rather t…
Reconstruct! Don't Encode: Self-Supervised Representation Reconstruction Loss for High-Intelligibility and Low-Latency Streaming Neural Audio Codec
Junhyeok Lee, Xiluo He, Jihwan Lee +6
Neural audio codecs optimized for mel-spectrogram reconstruction often fail to preserve intelligibility. While semantic encoder distillation improves encoded representations, it do…
MaskVCT: Masked Voice Codec Transformer for Zero-Shot Voice Conversion With Increased Controllability via Multiple Guidances
Junhyeok Lee, Helin Wang, Yaohan Guan +4
We introduce MaskVCT, a zero-shot voice conversion (VC) model that offers multi-factor controllability through multiple classifier-free guidances (CFGs). While previous VC models r…
CA-SSLR: Condition-Aware Self-Supervised Learning Representation for Generalized Speech Processing
Yen-Ju Lu, Jing Liu, Thomas Thebaud +4
We introduce Condition-Aware Self-Supervised Learning Representation (CA-SSLR), a generalist conditioning model broadly applicable to various speech-processing tasks. Compared to s…
Noise-robust Speech Separation with Fast Generative Correction
Helin Wang, Jesus Villalba, Laureano Moro-Velazquez +3
Speech separation, the task of isolating multiple speech sources from a mixed audio signal, remains challenging in noisy environments. In this paper, we propose a generative correc…
Improving fairness for spoken language understanding in atypical speech with Text-to-Speech
Helin Wang, Venkatesh Ravichandran, Milind Rao +8
Spoken language understanding (SLU) systems often exhibit suboptimal performance in processing atypical speech, typically caused by neurological conditions and motor impairments. R…
Leveraging Pretrained Image-text Models for Improving Audio-Visual Learning
Saurabhchand Bhati, Jesús Villalba, Laureano Moro-Velazquez +2
Visually grounded speech systems learn from paired images and their spoken captions. Recently, there have been attempts to utilize the visually grounded models trained from images…
DuTa-VC: A Duration-aware Typical-to-atypical Voice Conversion Approach with Diffusion Probabilistic Model
Helin Wang, Thomas Thebaud, Jesus Villalba +4
We present a novel typical-to-atypical voice conversion approach (DuTa-VC), which (i) can be trained with nonparallel data (ii) first introduces diffusion probabilistic model (iii)…
Regularizing Contrastive Predictive Coding for Speech Applications
Saurabhchand Bhati, Jesús Villalba, Piotr Żelasko +2
Self-supervised methods such as Contrastive predictive Coding (CPC) have greatly improved the quality of the unsupervised representations. These representations significantly reduc…
Non-Contrastive Self-supervised Learning for Utterance-Level Information Extraction from Speech
Jaejin Cho, Jes'us Villalba, Laureano Moro-Velazquez +1
In recent studies, self-supervised pre-trained models tend to outperform supervised pre-trained models in transfer learning. In particular, self-supervised learning (SSL) of uttera…
Non-Contrastive Self-Supervised Learning of Utterance-Level Speech Representations
Jaejin Cho, Raghavendra Pappagari, Piotr Żelasko +3
Considering the abundance of unlabeled speech data and the high labeling costs, unsupervised learning methods can be essential for better system development. One of the most succes…
Unsupervised Speech Segmentation and Variable Rate Representation Learning using Segmental Contrastive Predictive Coding
Saurabhchand Bhati, Jesús Villalba, Piotr Żelasko +2
Typically, unsupervised segmentation of speech into the phone and word-like units are treated as separate tasks and are often done via different methods which do not fully leverage…
Segmental Contrastive Predictive Coding for Unsupervised Word Segmentation
Saurabhchand Bhati, Jesús Villalba, Piotr Żelasko +2
Automatic detection of phoneme or word-like units is one of the core objectives in zero-resource speech processing. Recent attempts employ self-supervised training methods, such as…
Study of Pre-processing Defenses against Adversarial Attacks on State-of-the-art Speaker Recognition Systems
Sonal Joshi, Jesús Villalba, Piotr Żelasko +2
Adversarial examples to speaker recognition (SR) systems are generated by adding a carefully crafted noise to the speech signal to make the system fail while being imperceptible to…