216 citations · 556 across the 81 of their papers we have counts for
91 papers
Encoder-Free Multimodal LLMs Learn Implicit Encoders
David Harwath, Desh Raj, Xavier Velez +5
We empirically analyze two recently proposed encoder-free multimodal LLMs, Gemma-4 12B and Inkling-Small. Unlike previous multimodal LLMs, these models do not use modality-specific…
ASSET: Adaptive Speech Segmentation via Reinforcement Learning
Cihan Xiao, Yuan Tseng, Matthew Wiesner +4
Speech large language models (LLMs) are often built using pretrained acoustic encoders as an audio interface to an LLM decoder. However, LLMs scale poorly with input sequence lengt…
All In Good Time: Causality-Aware Framework for LLM-Based Simultaneous Speech-to-Speech Translation
Amir Hussein, Enas Albasiri, Travis M. Bartley +7
Large Language Models (LLMs) have shown strong performance in low-resource offline translation; however, extending them to simultaneous speech-to-speech translation (Simul-S2ST) re…
LAYERSCOPE: A Layerwise Characterization of Video and Multimodal Learned Representations
Sandra Arcos-Holzinger, Debashish Chakraborty, Rohita Mocharla +7
We propose LAYERSCOPE, a label-free, layerwise framework that aims to characterize a model's learned representations in video and multimodal settings. Evaluating downstream perform…
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…
Escape the Language Prior: Mitigating Late-Stage Modality Collapse in Audio Reasoning via Modality-Aware Policy Optimization
Cihan Xiao, Yiwen Shao, Chenxing Li +5
Audio and omni-modal large language models exhibit impressive cross-modal reasoning capabilities. However, standard reinforcement learning methods such as GRPO do not explicitly we…
Dimensionality-Aware Anomaly Detection in Learned Representations of Self-Supervised Speech Models
Sandra Arcos-Holzinger, Sarah M. Erfani, James Bailey +1
Self-supervised speech models (S3Ms) achieve strong downstream performance, yet their learned representations remain poorly understood under natural and adversarial perturbations.…
Modeling Overlapped Speech with Shuffles
Matthew Wiesner, Samuele Cornell, Alexander Polok +3
We propose to model parallel streams of data, such as overlapped speech, using shuffles. Specifically, this paper shows how the shuffle product and partial order finite-state autom…
Integrated Spoofing-Robust Automatic Speaker Verification via a Three-Class Formulation and LLR
Kai Tan, Lin Zhang, Ruiteng Zhang +6
Spoofing-robust automatic speaker verification (SASV) aims to integrate automatic speaker verification (ASV) and countermeasure (CM). A popular solution is fusion of independent AS…
Can LLMs Help Localize Fake Words in Partially Fake Speech?
Lin Zhang, Thomas Thebaud, Zexin Cai +5
Large language models (LLMs), trained on large-scale text, have recently attracted significant attention for their strong performance across many tasks. Motivated by this, we inves…
Universal Speech Content Factorization
Henry Li Xinyuan, Zexin Cai, Lin Zhang +5
We propose Universal Speech Content Factorization (USCF), a simple and invertible linear method for extracting a low-rank speech representation in which speaker timbre is suppresse…
SE-DiCoW: Self-Enrolled Diarization-Conditioned Whisper
Alexander Polok, Dominik Klement, Samuele Cornell +4
Speaker-attributed automatic speech recognition (ASR) in multi-speaker environments remains a major challenge. While some approaches achieve strong performance when fine-tuned on s…
SpatialEmb: Extract and Encode Spatial Information for 1-Stage Multi-channel Multi-speaker ASR on Arbitrary Microphone Arrays
Yiwen Shao, Yong Xu, Sanjeev Khudanpur +1
Spatial information is a critical clue for multi-channel multi-speaker target speech recognition. Most state-of-the-art multi-channel Automatic Speech Recognition (ASR) systems ext…
WST: Weakly Supervised Transducer for Automatic Speech Recognition
Dongji Gao, Chenda Liao, Changliang Liu +5
The Recurrent Neural Network-Transducer (RNN-T) is widely adopted in end-to-end (E2E) automatic speech recognition (ASR) tasks but depends heavily on large-scale, high-quality anno…
Unsupervised Speech Enhancement using Data-defined Priors
Dominik Klement, Matthew Maciejewski, Sanjeev Khudanpur +2
The majority of deep learning-based speech enhancement methods require paired clean-noisy speech data. Collecting such data at scale in real-world conditions is infeasible, which h…
Whisper-UT: A Unified Translation Framework for Speech and Text
Cihan Xiao, Matthew Wiesner, Debashish Chakraborty +7
Encoder-decoder models have achieved remarkable success in speech and text tasks, yet efficiently adapting these models to diverse uni/multi-modal scenarios remains an open challen…
CS-FLEURS: A Massively Multilingual and Code-Switched Speech Dataset
Brian Yan, Injy Hamed, Shuichiro Shimizu +24
We present CS-FLEURS, a new dataset for developing and evaluating code-switched speech recognition and translation systems beyond high-resourced languages. CS-FLEURS consists of 4…
Scalable Controllable Accented TTS
Henry Li Xinyuan, Zexin Cai, Ashi Garg +5
We tackle the challenge of scaling accented TTS systems, expanding their capabilities to include much larger amounts of training data and a wider variety of accent labels, even for…
CASPER: A Large Scale Spontaneous Speech Dataset
Cihan Xiao, Ruixing Liang, Xiangyu Zhang +8
The success of large language models has driven interest in developing similar speech processing capabilities. However, a key challenge is the scarcity of high-quality spontaneous…
HENT-SRT: Hierarchical Efficient Neural Transducer with Self-Distillation for Joint Speech Recognition and Translation
Amir Hussein, Cihan Xiao, Matthew Wiesner +3
Neural transducers (NT) provide an effective framework for speech streaming, demonstrating strong performance in automatic speech recognition (ASR). However, the application of NT…
ShiftySpeech: A Large-Scale Synthetic Speech Dataset with Distribution Shifts
Ashi Garg, Zexin Cai, Lin Zhang +6
The problem of synthetic speech detection has enjoyed considerable attention, with recent methods achieving low error rates across several established benchmarks. However, to what…
GenVC: Self-Supervised Zero-Shot Voice Conversion
Zexin Cai, Henry Li Xinyuan, Ashi Garg +5
Most current zero-shot voice conversion methods rely on externally supervised components, particularly speaker encoders, for training. To explore alternatives that eliminate this d…
DiCoW: Diarization-Conditioned Whisper for Target Speaker Automatic Speech Recognition
Alexander Polok, Dominik Klement, Martin Kocour +7
Speaker-attributed automatic speech recognition (ASR) in multi-speaker environments remains a significant challenge, particularly when systems conditioned on speaker embeddings fai…
HLTCOE JHU Submission to the Voice Privacy Challenge 2024
Henry Li Xinyuan, Zexin Cai, Ashi Garg +5
We present a number of systems for the Voice Privacy Challenge, including voice conversion based systems such as the kNN-VC method and the WavLM voice Conversion method, and text-t…