27 citations · 35 across the 11 of their papers we have counts for
18 papers
Path-Constrained Mixture-of-Experts
Zijin Gu, Tatiana Likhomanenko, Vimal Thilak +2
Sparse Mixture-of-Experts (MoE) architectures route each token through a subset of experts at each layer independently. We propose viewing MoE computation through the lens of \emph…
Which Data Matter? Embedding-Based Data Selection for Speech Recognition
Zakaria Aldeneh, Skyler Seto, Maureen de Seyssel +8
Modern ASR systems are typically trained on large-scale pseudo-labeled, in-the-wild data spanning multiple domains. While such heterogeneous data benefit generalist models designed…
Closing the Gap Between Text and Speech Understanding in LLMs
Santiago Cuervo, Skyler Seto, Maureen de Seyssel +5
Large Language Models (LLMs) can be adapted to extend their text capabilities to speech inputs. However, these speech-adapted LLMs consistently underperform their text-based counte…
Omni-Router: Sharing Routing Decisions in Sparse Mixture-of-Experts for Speech Recognition
Zijin Gu, Tatiana Likhomanenko, Navdeep Jaitly
Mixture-of-experts (MoE) architectures have expanded from language modeling to automatic speech recognition (ASR). Traditional MoE methods, such as the Switch Transformer, route ex…
SpeakStream: Streaming Text-to-Speech with Interleaved Data
Richard He Bai, Zijin Gu, Tatiana Likhomanenko +1
The latency bottleneck of traditional text-to-speech (TTS) systems fundamentally hinders the potential of streaming large language models (LLMs) in conversational AI. These TTS sys…
Mechanisms of Multimodal Synchronization: Insights from Decoder-Based Video-Text-to-Speech Synthesis
Akshita Gupta, Tatiana Likhomanenko, Karren Dai Yang +3
Unified decoder-only transformers have shown promise for multimodal generation, yet the mechanisms by which they synchronize modalities with heterogeneous sampling rates remain und…