40 citations · 71 across the 49 of their papers we have counts for
15 papers · 1 filter
Reproducing Whisper-Style Training Using an Open-Source Toolkit and Publicly Available Data
Yifan Peng, Jinchuan Tian, Brian Yan +13
Pre-training speech models on large volumes of data has achieved remarkable success. OpenAI Whisper is a multilingual multitask model trained on 680k hours of supervised speech dat…
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…
Joint Prediction and Denoising for Large-scale Multilingual Self-supervised Learning
William Chen, Jiatong Shi, Brian Yan +6
Multilingual self-supervised learning (SSL) has often lagged behind state-of-the-art (SOTA) methods due to the expenses and complexity required to handle many languages. This furth…
Dynamic-SUPERB: Towards A Dynamic, Collaborative, and Comprehensive Instruction-Tuning Benchmark for Speech
Chien-yu Huang, Ke-Han Lu, Shih-Heng Wang +12
Text language models have shown remarkable zero-shot capability in generalizing to unseen tasks when provided with well-formulated instructions. However, existing studies in speech…
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…
Reducing Barriers to Self-Supervised Learning: HuBERT Pre-training with Academic Compute
William Chen, Xuankai Chang, Yifan Peng +3
Self-supervised learning (SSL) has led to great strides in speech processing. However, the resources needed to train these models has become prohibitively large as they continue to…
Tensor decomposition for minimization of E2E SLU model toward on-device processing
Yosuke Kashiwagi, Siddhant Arora, Hayato Futami +6
Spoken Language Understanding (SLU) is a critical speech recognition application and is often deployed on edge devices. Consequently, on-device processing plays a significant role…
DPHuBERT: Joint Distillation and Pruning of Self-Supervised Speech Models
Yifan Peng, Yui Sudo, Shakeel Muhammad +1
Self-supervised learning (SSL) has achieved notable success in many speech processing tasks, but the large model size and heavy computational cost hinder the deployment. Knowledge…
A Comparative Study on E-Branchformer vs Conformer in Speech Recognition, Translation, and Understanding Tasks
Yifan Peng, Kwangyoun Kim, Felix Wu +7
Conformer, a convolution-augmented Transformer variant, has become the de facto encoder architecture for speech processing due to its superior performance in various tasks, includi…
The Pipeline System of ASR and NLU with MLM-based Data Augmentation toward STOP Low-resource Challenge
Hayato Futami, Jessica Huynh, Siddhant Arora +6
This paper describes our system for the low-resource domain adaptation track (Track 3) in Spoken Language Understanding Grand Challenge, which is a part of ICASSP Signal Processing…
A Study on the Integration of Pipeline and E2E SLU systems for Spoken Semantic Parsing toward STOP Quality Challenge
Siddhant Arora, Hayato Futami, Shih-Lun Wu +6
Recently there have been efforts to introduce new benchmark tasks for spoken language understanding (SLU), like semantic parsing. In this paper, we describe our proposed spoken sem…
ESPnet-ST-v2: Multipurpose Spoken Language Translation Toolkit
Brian Yan, Jiatong Shi, Yun Tang +13
ESPnet-ST-v2 is a revamp of the open-source ESPnet-ST toolkit necessitated by the broadening interests of the spoken language translation community. ESPnet-ST-v2 supports 1) offlin…
I3D: Transformer architectures with input-dependent dynamic depth for speech recognition
Yifan Peng, Jaesong Lee, Shinji Watanabe
Transformer-based end-to-end speech recognition has achieved great success. However, the large footprint and computational overhead make it difficult to deploy these models in some…
Structured Pruning of Self-Supervised Pre-trained Models for Speech Recognition and Understanding
Yifan Peng, Kwangyoun Kim, Felix Wu +2
Self-supervised speech representation learning (SSL) has shown to be effective in various downstream tasks, but SSL models are usually large and slow. Model compression techniques…
Improving Massively Multilingual ASR With Auxiliary CTC Objectives
William Chen, Brian Yan, Jiatong Shi +3
Multilingual Automatic Speech Recognition (ASR) models have extended the usability of speech technologies to a wide variety of languages. With how many languages these models have…