10 citations · 13 across the 20 of their papers we have counts for
22 papers
Long Listening Thoughts: Eliciting Open Auditory Reasoning with Deliberative Perception and Cognitive Refinement
Jaeyeon Kim, Chao-Han Huck Yang, Luoyi Zhang +5
We introduce Long Listening Thoughts (LLT), a framework for constructing post-training datasets that improves audio reasoning by pairing deliberate perceptual grounding with explic…
CrossEdit: Cross-Modal Training Enables Rich Audio-Visual Editing
William Chen, Prem Seetharaman, Ke Chen +10
Multimedia editing requires generative models to interpret complex, compositional instructions and modify only the desired elements across modalities, a capability largely beyond e…
Speech Language Models for Full-Meeting Speaker Diarization: Capabilities and Limitations
Jialu Li, Jinchuan Tian, Shinji Watanabe
Recent advances in Speech Language Models (SpeechLMs), which integrate large language models with speech foundation models, have enabled unified sequence modeling of speech process…
Listen to the Latents: Self-Correcting Speech Recognition in Large Audio Language Models Through Hidden-State Interactions
Chan-Jan Hsu, Jaeyeon Kim, Chao-Han Huck Yang +3
Recent automatic speech recognition (ASR) systems increasingly integrate large language models (LLMs) to leverage their semantic knowledge, either externally through logit fusion o…
An Efficient vLLM-Based Inference Pipeline for Unified Audio Understanding and Generation
Haoran Wang, Jinchuan Tian, Siddhant Arora +1
While Large Multimodal Models excel in comprehension, high-throughput inference engines lack native support for multimodal generation. This is severe in Speech Language Models, whe…
Comparative Reasoning: Making an Audio Language Model Better at Comparing Emotions
Abinay Reddy Naini, Jaeyeon Kim, Chao-Han Huck Yang +2
Large audio-language models (LALMs) can reason about audio, yet it remains unclear whether they can perform comparative judgments between two speech signals along emotional, enviro…
Bagpiper-TTS: Natural Language Guided Universal Speech Synthesis
Jinchuan Tian, Haoran Wang, Siddhant Arora +6
Classical TTS systems typically rely on rigid input formats and predefined metadata slots, limiting their ability to fulfill flexible user requirements. This paper introduces Bagpi…
ESPnet3: Infrastructure for Scalable Speech and Audio Research in the Foundation Model Era
Masao Someki, Alexander Polok, Carlos Carvalho +14
Recent speech research involves increasingly large datasets, complex models, and diverse experimental workflows. However, existing frameworks require substantial engineering effort…
Online Predictive Coding for Dual-Mode Self-Supervised Speech Model
Keita Goto, Takashi Maekaku, Jin Sakuma +3
Dual-mode self-supervised speech models are pre-trained to handle streaming and non-streaming conditions simultaneously. However, their attention is computed over different context…
Bagpiper-Edit: Zero-Shot Open-Ended Audio Editing via Rich-Caption
Xun Gong, Jinchuan Tian, Haoran Wang +3
Current text-guided audio editing methods rely on paired training data, predefined operation templates, and separate processing pipelines across speech, music, and sound. We presen…
Grounding Spoken LLMs in Multi-Speaker Audio via Diarization Conditioning
Alexander Polok, Samuele Cornell, Sathvik Udupa +3
We propose diarization-conditioned spoken language models (SLMs), a strategy for extending SLMs to far-field multi-talker audio. Rather than adapting the decoder via Serialized Out…
Evaluating Large Language Models Abilities for Addressee, Turn-change, and Next Speaker Prediction in Meetings
Ryo Fukuda, Takatomo Kano, Siddhant Arora +7
We investigate turn-taking in multimodal multi-party conversations using large language models (LLMs). We construct an evaluation framework for three tasks: addressee detection, tu…
Speech-Hands: A Self-Reflection Voice Agentic Approach to Speech Recognition and Audio Reasoning with Omni Perception
Zhen Wan, Chao-Han Huck Yang, Jinchuan Tian +15
We introduce a voice-agentic framework that learns one critical omni-understanding skill: knowing when to trust itself versus when to consult external audio perception. Our work is…
UALM: Unified Audio Language Model for Understanding, Generation and Reasoning
Jinchuan Tian, Sang-gil Lee, Zhifeng Kong +11
Recent advances in the audio language modeling (ALM) domain tackle audio understanding and text-to-audio generation as separate tasks. Very few studies attempt to unify these tasks…
SpeechIQ: Speech-Agentic Intelligence Quotient Across Cognitive Levels in Voice Understanding by Large Language Models
Zhen Wan, Chao-Han Huck Yang, Yahan Yu +8
We introduce Speech-based Intelligence Quotient (SIQ) as a new form of human cognition-inspired evaluation pipeline for voice understanding large language models, LLM Voice, design…
ESPnet-SpeechLM: An Open Speech Language Model Toolkit
Jinchuan Tian, Jiatong Shi, William Chen +13
We present ESPnet-SpeechLM, an open toolkit designed to democratize the development of speech language models (SpeechLMs) and voice-driven agentic applications. The toolkit standar…
Balancing Speech Understanding and Generation Using Continual Pre-training for Codec-based Speech LLM
Jiatong Shi, Chunlei Zhang, Jinchuan Tian +4
Recent advances in speech language models (LLMs) have extended textual LLMs to the speech domain, but balancing speech understanding and generation remains challenging, especially…
Large Language Model Based Generative Error Correction: A Challenge and Baselines for Speech Recognition, Speaker Tagging, and Emotion Recognition
Chao-Han Huck Yang, Taejin Park, Yuan Gong +18
Given recent advances in generative AI technology, a key question is how large language models (LLMs) can enhance acoustic modeling tasks using text decoding results from a frozen,…
FastAdaSP: Multitask-Adapted Efficient Inference for Large Speech Language Model
Yichen Lu, Jiaqi Song, Chao-Han Huck Yang +1
In this study, we aim to explore Multitask Speech Language Model (SpeechLM) efficient inference via token reduction. Unlike other modalities such as vision or text, speech has uniq…
VoiceTextBlender: Augmenting Large Language Models with Speech Capabilities via Single-Stage Joint Speech-Text Supervised Fine-Tuning
Yifan Peng, Krishna C. Puvvada, Zhehuai Chen +7
Recent studies have augmented large language models (LLMs) with speech capabilities, leading to the development of speech language models (SpeechLMs). Earlier SpeechLMs focused on…
Text-To-Speech Synthesis In The Wild
Jee-weon Jung, Wangyou Zhang, Soumi Maiti +11
Traditional Text-to-Speech (TTS) systems rely on studio-quality speech recorded in controlled settings.a Recently, an effort known as noisy-TTS training has emerged, aiming to util…
Generating Data with Text-to-Speech and Large-Language Models for Conversational Speech Recognition
Samuele Cornell, Jordan Darefsky, Zhiyao Duan +1
Currently, a common approach in many speech processing tasks is to leverage large scale pre-trained models by fine-tuning them on in-domain data for a particular application. Yet o…