2 citations · 2 across the 5 of their papers we have counts for
5 papers
AuRA: Internalizing Audio Understanding into LLMs as LoRA
Bo Cheng, Lei Shi, Zhanyu Ma +5
Recent efforts to extend large language models (LLMs) to speech inputs typically rely on cascaded ASR-LLM pipelines, end-to-end speech-language models, or bridge/distillation-based…
Chain-of-Glimpse: Search-Guided Progressive Object-Grounded Reasoning for Video Understanding
Zhixuan Wu, Quanxing Zha, Teng Wang +6
Video understanding requires identifying and reasoning over semantically discriminative visual objects across frames, yet existing object-agnostic solutions struggle to effectively…
Leveraging Parameter-Efficient Transfer Learning for Multi-Lingual Text-to-Speech Adaptation
Yingting Li, Ambuj Mehrish, Bryan Chew +2
Different languages have distinct phonetic systems and vary in their prosodic features making it challenging to develop a Text-to-Speech (TTS) model that can effectively synthesise…
HyperTTS: Parameter Efficient Adaptation in Text to Speech using Hypernetworks
Yingting Li, Rishabh Bhardwaj, Ambuj Mehrish +2
Neural speech synthesis, or text-to-speech (TTS), aims to transform a signal from the text domain to the speech domain. While developing TTS architectures that train and test on th…
CM-TTS: Enhancing Real Time Text-to-Speech Synthesis Efficiency through Weighted Samplers and Consistency Models
Xiang Li, Fan Bu, Ambuj Mehrish +4
Neural Text-to-Speech (TTS) systems find broad applications in voice assistants, e-learning, and audiobook creation. The pursuit of modern models, like Diffusion Models (DMs), hold…