19 citations · 53 across the 45 of their papers we have counts for
52 papers
ASSET: Adaptive Speech Segmentation via Reinforcement Learning
Cihan Xiao, Yuan Tseng, Matthew Wiesner +4
Speech large language models (LLMs) are often built using pretrained acoustic encoders as an audio interface to an LLM decoder. However, LLMs scale poorly with input sequence lengt…
RUBATO: Decoupled Clocks Allow Full-Duplex Models to Think as Fast as They Can
Yi-Jen Shih, Puyuan Peng, Haohe Liu +2
Recent Speech Full-Duplex (FDX) models are capable of engaging in naturalistic conversation by listening and speaking at the same time. Building on this foundation, recent architec…
Voices as Handles: Reasoning about Speaker Identity with Frozen Text LLMs
Runqiu Xu, Zhisheng Zheng, David Harwath
Multi-user voice agents must track who said what across dialogue sessions. Text LLMs are attractive backbones for such agents, but transcripts alone do not expose acoustic speaker…
SPADE: A Multilingual Dataset for Speech Partial Deepfake Detection and Localization
Yuan Tseng, Aishwarya Fursule, Andrew Zijun Ma +4
Recent improvements in voice-cloning speech generation systems raise concerns about misuse by malicious actors to impersonate others and spread misinformation. Detecting such tampe…
MP-Bench: Evaluating Voice Agents as a Multiparty Conversation Participant
Yi-Jen Shih, Shih-Yun Shan Kuan, Guan-Ting Lin +7
Conversational voice agents have advanced significantly, offering increasingly natural human-machine interactions through both cascaded and end-to-end architectures. However, while…
Quantifying Consonant Contributions to Word Intelligibility via Acoustic Masking
Eunjung Yeo, Kwanghee Choi, Krupaben Kothadia +4
Consonants contribute unequally to whether a word is understood. Given the limited time available for therapy, ranking consonants by contribution to intelligibility helps prioritiz…
RetroThinker: Enabling Retrospective Thinking in Speech LLMs
Yi-Jen Shih, Puyuan Peng, Abdelrahman Mohamed +1
Speech large language models (SpeechLLMs) offer reduced latency and retain paralinguistic nuances that are typically lost in cascaded automatic speech recognition (ASR) and text-ba…
CtrlSpeech: Coarse-to-Fine Control for Expressive Speech Synthesis
Zhisheng Zheng, Xiaohang Sun, Zhu Liu +5
Recent Text-To-Speech (TTS) systems have achieved strong naturalness and zero-shot voice cloning performance, but fine-grained control of expressive speech at the word or phoneme l…
Phone Segmentation and Recognition through Phonological Activation Mapping
Shikhar Bharadwaj, Kwanghee Choi, Stephen McIntosh +8
Phone segmentation and recognition are inherently related tasks, yet modern approaches typically model them separately. We argue that phonetic structure is already latent in the re…
Linear Script Representations in Speech Foundation Models Enable Zero-Shot Transliteration
Ryan Soh-Eun Shim, Kwanghee Choi, Kalvin Chang +8
Multilingual speech foundation models such as Whisper are trained on web-scale data, where data for each language consists of a myriad of regional varieties. However, different reg…
FacEDiT: Unified Talking Face Editing and Generation via Facial Motion Infilling
Kim Sung-Bin, Joohyun Chang, David Harwath +1
Talking face editing and face generation have often been studied as distinct problems. In this work, we propose viewing both not as separate tasks but as subtasks of a unifying for…
Codec2Vec: Self-Supervised Speech Representation Learning Using Neural Speech Codecs
Wei-Cheng Tseng, David Harwath
Recent advancements in neural audio codecs have not only enabled superior audio compression but also enhanced speech synthesis techniques. Researchers are now exploring their poten…
VoiceCraft-X: Unifying Multilingual, Voice-Cloning Speech Synthesis and Speech Editing
Zhisheng Zheng, Puyuan Peng, Anuj Diwan +5
We introduce VoiceCraft-X, an autoregressive neural codec language model which unifies multilingual speech editing and zero-shot Text-to-Speech (TTS) synthesis across 11 languages:…
Unifying Model and Layer Fusion for Speech Foundation Models
Yi-Jen Shih, David Harwath
Speech Foundation Models have gained significant attention recently. Prior works have shown that the fusion of representations from multiple layers of the same model or the fusion…
RosettaSpeech: Zero-Shot Speech-to-Speech Translation without Parallel Speech
Zhisheng Zheng, Xiaohang Sun, Tuan Dinh +8
End-to-end speech-to-speech translation (S2ST) systems typically struggle with a critical data bottleneck: the scarcity of parallel speech-to-speech corpora. To overcome this, we i…
MMAU-Pro: A Challenging and Comprehensive Benchmark for Holistic Evaluation of Audio General Intelligence
Sonal Kumar, Šimon Sedláček, Vaibhavi Lokegaonkar +31
Audio comprehension-including speech, non-speech sounds, and music-is essential for achieving human-level intelligence. Consequently, AI agents must demonstrate holistic audio unde…
VoiceStar: Robust Zero-Shot Autoregressive TTS with Duration Control and Extrapolation
Puyuan Peng, Shang-Wen Li, Abdelrahman Mohamed +1
We present VoiceStar, the first zero-shot TTS model that achieves both output duration control and extrapolation. VoiceStar is an autoregressive encoder-decoder neural codec langua…
Probing the Robustness Properties of Neural Speech Codecs
Wei-Cheng Tseng, David Harwath
Neural speech codecs have revolutionized speech coding, achieving higher compression while preserving audio fidelity. Beyond compression, they have emerged as tokenization strategi…
Rhapsody: A Dataset for Highlight Detection in Podcasts
Younghan Park, Anuj Diwan, David Harwath +1
Podcasts have become daily companions for half a billion users. Given the enormous amount of podcast content available, highlights provide a valuable signal that helps viewers get…
VoiceCraft-Dub: Automated Video Dubbing with Neural Codec Language Models
Kim Sung-Bin, Jeongsoo Choi, Puyuan Peng +3
We present VoiceCraft-Dub, a novel approach for automated video dubbing that synthesizes high-quality speech from text and facial cues. This task has broad applications in filmmaki…
Scaling Rich Style-Prompted Text-to-Speech Datasets
Anuj Diwan, Zhisheng Zheng, David Harwath +1
We introduce Paralinguistic Speech Captions (ParaSpeechCaps), a large-scale dataset that annotates speech utterances with rich style captions. While rich abstract tags (e.g. guttur…
How to Learn a New Language? An Efficient Solution for Self-Supervised Learning Models Unseen Languages Adaption in Low-Resource Scenario
Shih-Heng Wang, Zih-Ching Chen, Jiatong Shi +6
The utilization of speech Self-Supervised Learning (SSL) models achieves impressive performance on Automatic Speech Recognition (ASR). However, in low-resource language ASR, they e…
Dynamic-SUPERB Phase-2: A Collaboratively Expanding Benchmark for Measuring the Capabilities of Spoken Language Models with 180 Tasks
Chien-yu Huang, Wei-Chih Chen, Shu-wen Yang +77
Multimodal foundation models, such as Gemini and ChatGPT, have revolutionized human-machine interactions by seamlessly integrating various forms of data. Developing a universal spo…
SyllableLM: Learning Coarse Semantic Units for Speech Language Models
Alan Baade, Puyuan Peng, David Harwath
Language models require tokenized inputs. However, tokenization strategies for continuous data like audio and vision are often based on simple heuristics such as fixed sized convol…