41 citations · 47 across the 7 of their papers we have counts for
7 papers
Alignment-Free Text-Audiobox for Voice Dubbing and Full-Duplex Dialogue Synthesis
Sanyuan Chen, Min-Jae Hwang, Sho Inoue +12
We present Alignment-Free Text-Audiobox (Text-AB), a unified framework for high-quality voice dubbing and full-duplex dialogue synthesis. Building on a Diffusion Transformer traine…
Seamless Interaction: Dyadic Audiovisual Motion Modeling and Large-Scale Dataset
Vasu Agrawal, Akinniyi Akinyemi, Kathryn Alvero +81
Human communication involves a complex interplay of verbal and nonverbal signals, essential for conveying meaning and achieving interpersonal goals. To develop socially intelligent…
SSR: Alignment-Aware Modality Connector for Speech Language Models
Weiting Tan, Hirofumi Inaguma, Ning Dong +2
Fusing speech into pre-trained language model (SpeechLM) usually suffers from inefficient encoding of long-form speech and catastrophic forgetting of pre-trained text modality. We…
MMM: Multi-Layer Multi-Residual Multi-Stream Discrete Speech Representation from Self-supervised Learning Model
Jiatong Shi, Xutai Ma, Hirofumi Inaguma +2
Speech discrete representation has proven effective in various downstream applications due to its superior compression rate of the waveform, fast convergence during training, and c…
Seamless: Multilingual Expressive and Streaming Speech Translation
Seamless Communication, Loïc Barrault, Yu-An Chung +62
Large-scale automatic speech translation systems today lack key features that help machine-mediated communication feel seamless when compared to human-to-human dialogue. In this wo…
Efficient Monotonic Multihead Attention
Xutai Ma, Anna Sun, Siqi Ouyang +2
We introduce the Efficient Monotonic Multihead Attention (EMMA), a state-of-the-art simultaneous translation model with numerically-stable and unbiased monotonic alignment estimati…
Multi-resolution HuBERT: Multi-resolution Speech Self-Supervised Learning with Masked Unit Prediction
Jiatong Shi, Hirofumi Inaguma, Xutai Ma +2
Existing Self-Supervised Learning (SSL) models for speech typically process speech signals at a fixed resolution of 20 milliseconds. This approach overlooks the varying information…