4 citations · 18 across the 25 of their papers we have counts for
11 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…
Evaluating Speech Synthesis by Training Recognizers on Synthetic Speech
Dareen Alharthi, Roshan Sharma, Hira Dhamyal +3
Modern speech synthesis systems have improved significantly, with synthetic speech being indistinguishable from real speech. However, efficient and holistic evaluation of synthetic…
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…
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…
Towards Practical and Efficient Image-to-Speech Captioning with Vision-Language Pre-training and Multi-modal Tokens
Minsu Kim, Jeongsoo Choi, Soumi Maiti +3
In this paper, we propose methods to build a powerful and efficient Image-to-Speech captioning (Im2Sp) model. To this end, we start with importing the rich knowledge related to ima…
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…
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…
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…
Unsupervised Data Selection for TTS: Using Arabic Broadcast News as a Case Study
Massa Baali, Tomoki Hayashi, Hamdy Mubarak +4
Several high-resource Text to Speech (TTS) systems currently produce natural, well-established human-like speech. In contrast, low-resource languages, including Arabic, have very l…
Learning to Speak from Text: Zero-Shot Multilingual Text-to-Speech with Unsupervised Text Pretraining
Takaaki Saeki, Soumi Maiti, Xinjian Li +3
While neural text-to-speech (TTS) has achieved human-like natural synthetic speech, multilingual TTS systems are limited to resource-rich languages due to the need for paired text…