22 citations · 43 across the 23 of their papers we have counts for
23 papers
Which Data Matter? Embedding-Based Data Selection for Speech Recognition
Zakaria Aldeneh, Skyler Seto, Maureen de Seyssel +8
Modern ASR systems are typically trained on large-scale pseudo-labeled, in-the-wild data spanning multiple domains. While such heterogeneous data benefit generalist models designed…
Chain-of-Thought Training for Open E2E Spoken Dialogue Systems
Siddhant Arora, Jinchuan Tian, Hayato Futami +5
Unlike traditional cascaded pipelines, end-to-end (E2E) spoken dialogue systems preserve full differentiability and capture non-phonemic information, making them well-suited for mo…
Context-Driven Dynamic Pruning for Large Speech Foundation Models
Masao Someki, Shikhar Bharadwaj, Atharva Anand Joshi +7
Speech foundation models achieve strong generalization across languages and acoustic conditions, but require significant computational resources for inference. In the context of sp…
SpoofCeleb: Speech Deepfake Detection and SASV In The Wild
Jee-weon Jung, Yihan Wu, Xin Wang +11
This paper introduces SpoofCeleb, a dataset designed for Speech Deepfake Detection (SDD) and Spoofing-robust Automatic Speaker Verification (SASV), utilizing source data from real-…
ESPnet-Codec: Comprehensive Training and Evaluation of Neural Codecs for Audio, Music, and Speech
Jiatong Shi, Jinchuan Tian, Yihan Wu +17
Neural codecs have become crucial to recent speech and audio generation research. In addition to signal compression capabilities, discrete codecs have also been found to enhance do…
Speaker-IPL: Unsupervised Learning of Speaker Characteristics with i-Vector based Pseudo-Labels
Zakaria Aldeneh, Takuya Higuchi, Jee-weon Jung +6
Iterative self-training, or iterative pseudo-labeling (IPL) -- using an improved model from the current iteration to provide pseudo-labels for the next iteration -- has proven to b…
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…
The VoxCeleb Speaker Recognition Challenge: A Retrospective
Jaesung Huh, Joon Son Chung, Arsha Nagrani +4
The VoxCeleb Speaker Recognition Challenges (VoxSRC) were a series of challenges and workshops that ran annually from 2019 to 2023. The challenges primarily evaluated the tasks of…
On the Evaluation of Speech Foundation Models for Spoken Language Understanding
Siddhant Arora, Ankita Pasad, Chung-Ming Chien +9
The Spoken Language Understanding Evaluation (SLUE) suite of benchmark tasks was recently introduced to address the need for open resources and benchmarking of complex spoken langu…
Beyond Performance Plateaus: A Comprehensive Study on Scalability in Speech Enhancement
Wangyou Zhang, Kohei Saijo, Jee-weon Jung +3
Deep learning-based speech enhancement (SE) models have achieved impressive performance in the past decade. Numerous advanced architectures have been designed to deliver state-of-t…
Beyond Silence: Bias Analysis through Loss and Asymmetric Approach in Audio Anti-Spoofing
Hye-jin Shim, Md Sahidullah, Jee-weon Jung +2
Current trends in audio anti-spoofing detection research strive to improve models' ability to generalize across unseen attacks by learning to identify a variety of spoofing artifac…
Improving Design of Input Condition Invariant Speech Enhancement
Wangyou Zhang, Jee-weon Jung, Shinji Watanabe +1
Building a single universal speech enhancement (SE) system that can handle arbitrary input is a demanded but underexplored research topic. Towards this ultimate goal, one direction…
TMT: Tri-Modal Translation between Speech, Image, and Text by Processing Different Modalities as Different Languages
Minsu Kim, Jee-weon Jung, Hyeongseop Rha +5
The capability to jointly process multi-modal information is becoming an essential task. However, the limited number of paired multi-modal data and the large computational requirem…
Can you Remove the Downstream Model for Speaker Recognition with Self-Supervised Speech Features?
Zakaria Aldeneh, Takuya Higuchi, Jee-weon Jung +6
Self-supervised features are typically used in place of filter-bank features in speaker verification models. However, these models were originally designed to ingest filter-bank fe…
AugSumm: towards generalizable speech summarization using synthetic labels from large language model
Jee-weon Jung, Roshan Sharma, William Chen +2
Abstractive speech summarization (SSUM) aims to generate human-like summaries from speech. Given variations in information captured and phrasing, recordings can be summarized in mu…
ESPnet-SPK: full pipeline speaker embedding toolkit with reproducible recipes, self-supervised front-ends, and off-the-shelf models
Jee-weon Jung, Wangyou Zhang, Jiatong Shi +5
This paper introduces ESPnet-SPK, a toolkit designed with several objectives for training speaker embedding extractors. First, we provide an open-source platform for researchers in…
OWSM v3.1: Better and Faster Open Whisper-Style Speech Models based on E-Branchformer
Yifan Peng, Jinchuan Tian, William Chen +9
Recent studies have highlighted the importance of fully open foundation models. The Open Whisper-style Speech Model (OWSM) is an initial step towards reproducing OpenAI Whisper usi…
Understanding Probe Behaviors through Variational Bounds of Mutual Information
Kwanghee Choi, Jee-weon Jung, Shinji Watanabe
With the success of self-supervised representations, researchers seek a better understanding of the information encapsulated within a representation. Among various interpretability…
One model to rule them all ? Towards End-to-End Joint Speaker Diarization and Speech Recognition
Samuele Cornell, Jee-weon Jung, Shinji Watanabe +1
This paper presents a novel framework for joint speaker diarization (SD) and automatic speech recognition (ASR), named SLIDAR (sliding-window diarization-augmented recognition). SL…
UniverSLU: Universal Spoken Language Understanding for Diverse Tasks with Natural Language Instructions
Siddhant Arora, Hayato Futami, Jee-weon Jung +6
Recent studies leverage large language models with multi-tasking capabilities, using natural language prompts to guide the model's behavior and surpassing performance of task-speci…
Exploring Speech Recognition, Translation, and Understanding with Discrete Speech Units: A Comparative Study
Xuankai Chang, Brian Yan, Kwanghee Choi +14
Speech signals, typically sampled at rates in the tens of thousands per second, contain redundancies, evoking inefficiencies in sequence modeling. High-dimensional speech features…
Improving Audio Captioning Models with Fine-grained Audio Features, Text Embedding Supervision, and LLM Mix-up Augmentation
Shih-Lun Wu, Xuankai Chang, Gordon Wichern +4
Automated audio captioning (AAC) aims to generate informative descriptions for various sounds from nature and/or human activities. In recent years, AAC has quickly attracted resear…
Voxtlm: unified decoder-only models for consolidating speech recognition/synthesis and speech/text continuation tasks
Soumi Maiti, Yifan Peng, Shukjae Choi +3
We propose a decoder-only language model, VoxtLM, that can perform four tasks: speech recognition, speech synthesis, text generation, and speech continuation. VoxtLM integrates tex…