90 citations · 178 across the 45 of their papers we have counts for
57 papers
Who Are They to Each Other? Multi-Agent Reasoning for Speaker Relationship Inference
Yaohan Guan, Yen-Ju Lu, Yuzhe Wang +5
Inferring speaker relationships from spoken conversations is an important step towards socially aware speech understanding. However, this task remains underexplored, and supervised…
When Text Misleads: Inconsistent-Aware Reasoning for Audio-Grounded Dialogue
Yen-Ju Lu, Yuzhe Wang, Yaohan Guan +8
Understanding spoken dialogue requires joint reasoning over lexical content and paralinguistic acoustic signals such as emotion and conversational intent. However, existing evaluat…
Clustering Unsupervised Representations as Defense against Poisoning Attacks on Speech Commands Classification System
Thomas Thebaud, Sonal Joshi, Henry Li +4
Poisoning attacks entail attackers intentionally tampering with training data. In this paper, we consider a dirty-label poisoning attack scenario on a speech commands classificatio…
DiT-Flow: Speech Enhancement Robust to Multiple Distortions based on Flow Matching in Latent Space and Diffusion Transformers
Tianyu Cao, Helin Wang, Ari Frummer +7
Recent advances in generative models, such as diffusion and flow matching, have shown strong performance in audio tasks. However, speech enhancement (SE) models are typically train…
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…
Spoken DialogSum: An Emotion-Rich Conversational Dataset for Spoken Dialogue Summarization
Yen-Ju Lu, Kunxiao Gao, Mingrui Liang +5
Recent audio language models can follow long conversations. However, research on emotion-aware or spoken dialogue summarization is constrained by the lack of data that links speech…
ReFESS-QI: Reference-Free Evaluation For Speech Separation With Joint Quality And Intelligibility Scoring
Ari Frummer, Helin Wang, Tianyu Cao +6
Source separation is a crucial pre-processing step for various speech processing tasks, such as automatic speech recognition (ASR). Traditionally, the evaluation metrics for speech…
Scaling Multi-Talker ASR with Speaker-Agnostic Activity Streams
Xiluo He, Alexander Polok, Jesús Villalba +2
An increasingly common training paradigm for multi-talker automatic speech recognition (ASR) is to use speaker activity signals to adapt single-speaker ASR models for overlapping s…
Where Do Backdoors Live? A Component-Level Analysis of Backdoor Propagation in Speech Language Models
Alexandrine Fortier, Thomas Thebaud, Jesús Villalba +3
Speech language models (SLMs) are systems of systems: independent components that unite to achieve a common goal. Despite their heterogeneous nature, SLMs are often studied end-to-…
Paired by the Teacher: Turning Unpaired Data into High-Fidelity Pairs for Low-Resource Text Generation
Yen-Ju Lu, Thomas Thebaud, Laureano Moro-Velazquez +2
We present Paired by the Teacher (PbT), a two-stage teacher-student pipeline that synthesizes accurate input-output pairs without human labels or parallel data. In many low-resourc…
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…
CapSpeech: Enabling Downstream Applications in Style-Captioned Text-to-Speech
Helin Wang, Jiarui Hai, Dading Chong +11
Recent advancements in generative artificial intelligence have significantly transformed the field of style-captioned text-to-speech synthesis (CapTTS). However, adapting CapTTS to…
SoloSpeech: Enhancing Intelligibility and Quality in Target Speech Extraction through a Cascaded Generative Pipeline
Helin Wang, Jiarui Hai, Dongchao Yang +7
Target Speech Extraction (TSE) aims to isolate a target speaker's voice from a mixture of multiple speakers by leveraging speaker-specific cues, typically provided as auxiliary aud…
Detecting Neurodegenerative Diseases using Frame-Level Handwriting Embeddings
Sarah Laouedj, Yuzhe Wang, Jesus Villalba +3
In this study, we explored the use of spectrograms to represent handwriting signals for assessing neurodegenerative diseases, including 42 healthy controls (CTL), 35 subjects with…
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…
Clean Label Attacks against SLU Systems
Henry Li Xinyuan, Sonal Joshi, Thomas Thebaud +3
Poisoning backdoor attacks involve an adversary manipulating the training data to induce certain behaviors in the victim model by inserting a trigger in the signal at inference tim…
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…
Unraveling Adversarial Examples against Speaker Identification -- Techniques for Attack Detection and Victim Model Classification
Sonal Joshi, Thomas Thebaud, Jesús Villalba +1
Adversarial examples have proven to threaten speaker identification systems, and several countermeasures against them have been proposed. In this paper, we propose a method to dete…
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…
Stabilized training of joint energy-based models and their practical applications
Martin Sustek, Samik Sadhu, Lukas Burget +4
The recently proposed Joint Energy-based Model (JEM) interprets discriminatively trained classifier as an energy model, which is also trained as a generative model describ…
Self-FiLM: Conditioning GANs with self-supervised representations for bandwidth extension based speaker recognition
Saurabh Kataria, Jesús Villalba, Laureano Moro-Velázquez +2
Speech super-resolution/Bandwidth Extension (BWE) can improve downstream tasks like Automatic Speaker Verification (ASV). We introduce a simple novel technique called Self-FiLM to…
Time-domain speech super-resolution with GAN based modeling for telephony speaker verification
Saurabh Kataria, Jesús Villalba, Laureano Moro-Velázquez +2
Automatic Speaker Verification (ASV) technology has become commonplace in virtual assistants. However, its performance suffers when there is a mismatch between the train and test d…