2.1k citations · 3.3k across the 55 of their papers we have counts for
10 papers · 1 filter
Large Language Models as Neurolinguistic Subjects: Discrepancy between Performance and Competence
Linyang He, Ercong Nie, Helmut Schmid +3
This study investigates the linguistic understanding of Large Language Models (LLMs) regarding signifier (form) and signified (meaning) by distinguishing two LLM assessment paradig…
Just ASR + LLM? A Study on Speech Large Language Models' Ability to Identify and Understand Speaker in Spoken Dialogue
Junkai Wu, Xulin Fan, Bo-Ru Lu +4
In recent years, we have observed a rapid advancement in speech language models (SpeechLLMs), catching up with humans' listening and reasoning abilities. SpeechLLMs have demonstrat…
StyleTTS-ZS: Efficient High-Quality Zero-Shot Text-to-Speech Synthesis with Distilled Time-Varying Style Diffusion
Yinghao Aaron Li, Xilin Jiang, Cong Han +1
The rapid development of large-scale text-to-speech (TTS) models has led to significant advancements in modeling diverse speaker prosody and voices. However, these models often fac…
Style-Talker: Finetuning Audio Language Model and Style-Based Text-to-Speech Model for Fast Spoken Dialogue Generation
Yinghao Aaron Li, Xilin Jiang, Jordan Darefsky +2
The rapid advancement of large language models (LLMs) has significantly propelled the development of text-based chatbots, demonstrating their capability to engage in coherent and c…
DeepSpeech models show Human-like Performance and Processing of Cochlear Implant Inputs
Cynthia R. Steinhardt, Menoua Keshishian, Nima Mesgarani +1
Cochlear implants(CIs) are arguably the most successful neural implant, having restored hearing to over one million people worldwide. While CI research has focused on modeling the…
Speech Slytherin: Examining the Performance and Efficiency of Mamba for Speech Separation, Recognition, and Synthesis
Xilin Jiang, Yinghao Aaron Li, Adrian Nicolas Florea +2
It is too early to conclude that Mamba is a better alternative to transformers for speech before comparing Mamba with transformers in terms of both performance and efficiency in mu…
SSAMBA: Self-Supervised Audio Representation Learning with Mamba State Space Model
Siavash Shams, Sukru Samet Dindar, Xilin Jiang +1
Transformers have revolutionized deep learning across various tasks, including audio representation learning, due to their powerful modeling capabilities. However, they often suffe…
Dual-path Mamba: Short and Long-term Bidirectional Selective Structured State Space Models for Speech Separation
Xilin Jiang, Cong Han, Nima Mesgarani
Transformers have been the most successful architecture for various speech modeling tasks, including speech separation. However, the self-attention mechanism in transformers with q…
Listen, Chat, and Remix: Text-Guided Soundscape Remixing for Enhanced Auditory Experience
Xilin Jiang, Cong Han, Yinghao Aaron Li +1
In daily life, we encounter a variety of sounds, both desirable and undesirable, with limited control over their presence and volume. Our work introduces "Listen, Chat, and Remix"…
Contextual Feature Extraction Hierarchies Converge in Large Language Models and the Brain
Gavin Mischler, Yinghao Aaron Li, Stephan Bickel +2
Recent advancements in artificial intelligence have sparked interest in the parallels between large language models (LLMs) and human neural processing, particularly in language com…