18 papers
LERD: Latent Event-Relational Dynamics for Neurodegenerative Classification
Yicheng Feng, Hairong Chen, Ziyu Jia +2
Alzheimer's disease (AD) alters brain electrophysiology and disrupts multichannel EEG dynamics, making accurate and clinically useful EEG-based diagnosis increasingly important for…
Discrete Diffusion for Generative Modeling of Text-Aligned Speech Tokens
Pin-Jui Ku, He Huang, Jean-Marie Lemercier +3
This paper introduces a discrete diffusion model (DDM) framework for text-aligned speech tokenization and reconstruction. By replacing the auto-regressive speech decoder with a dis…
TalkVid: A Large-Scale Diversified Dataset for Audio-Driven Talking Head Synthesis
Shunian Chen, Hejin Huang, Yexin Liu +10
Audio-driven talking head synthesis has achieved remarkable photorealism, yet state-of-the-art (SOTA) models exhibit a critical failure: they lack generalization to the full spectr…
Streaming Sortformer: Speaker Cache-Based Online Speaker Diarization with Arrival-Time Ordering
Ivan Medennikov, Taejin Park, Weiqing Wang +5
This paper presents a streaming extension for the Sortformer speaker diarization framework, whose key property is the arrival-time ordering of output speakers. The proposed approac…
Recent Trends in Distant Conversational Speech Recognition: A Review of CHiME-7 and 8 DASR Challenges
Samuele Cornell, Christoph Boeddeker, Taejin Park +9
The CHiME-7 and 8 distant speech recognition (DASR) challenges focus on multi-channel, generalizable, joint automatic speech recognition (ASR) and diarization of conversational spe…
Speaker Targeting via Self-Speaker Adaptation for Multi-talker ASR
Weiqing Wang, Taejin Park, Ivan Medennikov +6
We propose a self-speaker adaptation method for streaming multi-talker automatic speech recognition (ASR) that eliminates the need for explicit speaker queries. Unlike conventional…
Word Level Timestamp Generation for Automatic Speech Recognition and Translation
Ke Hu, Krishna Puvvada, Elena Rastorgueva +7
We introduce a data-driven approach for enabling word-level timestamp prediction in the Canary model. Accurate timestamp information is crucial for a variety of downstream tasks su…
Granary: Speech Recognition and Translation Dataset in 25 European Languages
Nithin Rao Koluguri, Monica Sekoyan, George Zelenfroynd +12
Multi-task and multilingual approaches benefit large models, yet speech processing for low-resource languages remains underexplored due to data scarcity. To address this, we presen…
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…
META-CAT: Speaker-Informed Speech Embeddings via Meta Information Concatenation for Multi-talker ASR
Jinhan Wang, Weiqing Wang, Kunal Dhawan +7
We propose a novel end-to-end multi-talker automatic speech recognition (ASR) framework that enables both multi-speaker (MS) ASR and target-speaker (TS) ASR. Our proposed model is…
Sortformer: A Novel Approach for Permutation-Resolved Speaker Supervision in Speech-to-Text Systems
Taejin Park, Ivan Medennikov, Kunal Dhawan +6
Sortformer is an encoder-based speaker diarization model designed for supervising speaker tagging in speech-to-text models. Instead of relying solely on permutation invariant loss…
Resource-Efficient Adaptation of Speech Foundation Models for Multi-Speaker ASR
Weiqing Wang, Kunal Dhawan, Taejin Park +6
Speech foundation models have achieved state-of-the-art (SoTA) performance across various tasks, such as automatic speech recognition (ASR) in hundreds of languages. However, multi…
NEST: Self-supervised Fast Conformer as All-purpose Seasoning to Speech Processing Tasks
He Huang, Taejin Park, Kunal Dhawan +6
Self-supervised learning has been proved to benefit a wide range of speech processing tasks, such as speech recognition/translation, speaker verification and diarization, etc. Howe…
BESTOW: Efficient and Streamable Speech Language Model with the Best of Two Worlds in GPT and T5
Zhehuai Chen, He Huang, Oleksii Hrinchuk +5
Incorporating speech understanding capabilities into pretrained large-language models has become a vital research direction (SpeechLLM). The previous architectures can be categoriz…
DeSTA: Enhancing Speech Language Models through Descriptive Speech-Text Alignment
Ke-Han Lu, Zhehuai Chen, Szu-Wei Fu +4
Recent speech language models (SLMs) typically incorporate pre-trained speech models to extend the capabilities from large language models (LLMs). In this paper, we propose a Descr…
The CHiME-7 Challenge: System Description and Performance of NeMo Team's DASR System
Tae Jin Park, He Huang, Ante Jukic +7
We present the NVIDIA NeMo team's multi-channel speech recognition system for the 7th CHiME Challenge Distant Automatic Speech Recognition (DASR) Task, focusing on the development…
Property-Aware Multi-Speaker Data Simulation: A Probabilistic Modelling Technique for Synthetic Data Generation
Tae Jin Park, He Huang, Coleman Hooper +5
We introduce a sophisticated multi-speaker speech data simulator, specifically engineered to generate multi-speaker speech recordings. A notable feature of this simulator is its ca…
SALM: Speech-augmented Language Model with In-context Learning for Speech Recognition and Translation
Zhehuai Chen, He Huang, Andrei Andrusenko +6
We present a novel Speech Augmented Language Model (SALM) with {\em multitask} and {\em in-context} learning capabilities. SALM comprises a frozen text LLM, a audio encoder, a moda…