275 citations · 356 across the 16 of their papers we have counts for
16 papers
Humans Hallucinate Too: Language Models Identify and Correct Subjective Annotation Errors With Label-in-a-Haystack Prompts
Georgios Chochlakis, Peter Wu, Arjun Bedi +3
Modeling complex subjective tasks in Natural Language Processing, such as recognizing emotion and morality, is considerably challenging due to significant variation in human annota…
Deep Speech Synthesis from MRI-Based Articulatory Representations
Peter Wu, Tingle Li, Yijing Lu +6
In this paper, we study articulatory synthesis, a speech synthesis method using human vocal tract information that offers a way to develop efficient, generalizable and interpretabl…
Speaker-Independent Acoustic-to-Articulatory Speech Inversion
Peter Wu, Li-Wei Chen, Cheol Jun Cho +4
To build speech processing methods that can handle speech as naturally as humans, researchers have explored multiple ways of building an invertible mapping from speech to an interp…
A Fast and Accurate Pitch Estimation Algorithm Based on the Pseudo Wigner-Ville Distribution
Yisi Liu, Peter Wu, Alan W Black +1
Estimation of fundamental frequency (F0) in voiced segments of speech signals, also known as pitch tracking, plays a crucial role in pitch synchronous speech analysis, speech synth…
Articulation GAN: Unsupervised modeling of articulatory learning
Gašper Beguš, Alan Zhou, Peter Wu +1
Generative deep neural networks are widely used for speech synthesis, but most existing models directly generate waveforms or spectral outputs. Humans, however, produce speech by c…
Evidence of Vocal Tract Articulation in Self-Supervised Learning of Speech
Cheol Jun Cho, Peter Wu, Abdelrahman Mohamed +1
Recent self-supervised learning (SSL) models have proven to learn rich representations of speech, which can readily be utilized by diverse downstream tasks. To understand such util…
Deep Speech Synthesis from Articulatory Representations
Peter Wu, Shinji Watanabe, Louis Goldstein +2
In the articulatory synthesis task, speech is synthesized from input features containing information about the physical behavior of the human vocal tract. This task provides a prom…
Muskits: an End-to-End Music Processing Toolkit for Singing Voice Synthesis
Jiatong Shi, Shuai Guo, Tao Qian +9
This paper introduces a new open-source platform named Muskits for end-to-end music processing, which mainly focuses on end-to-end singing voice synthesis (E2E-SVS). Muskits suppor…
PACS: A Dataset for Physical Audiovisual CommonSense Reasoning
Samuel Yu, Peter Wu, Paul Pu Liang +2
In order for AI to be safely deployed in real-world scenarios such as hospitals, schools, and the workplace, it must be able to robustly reason about the physical world. Fundamenta…
Cross-lingual Transfer for Speech Processing using Acoustic Language Similarity
Peter Wu, Jiatong Shi, Yifan Zhong +2
Speech processing systems currently do not support the vast majority of languages, in part due to the lack of data in low-resource languages. Cross-lingual transfer offers a compel…
ESPnet2-TTS: Extending the Edge of TTS Research
Tomoki Hayashi, Ryuichi Yamamoto, Takenori Yoshimura +7
This paper describes ESPnet2-TTS, an end-to-end text-to-speech (E2E-TTS) toolkit. ESPnet2-TTS extends our earlier version, ESPnet-TTS, by adding many new features, including: on-th…
MultiBench: Multiscale Benchmarks for Multimodal Representation Learning
Paul Pu Liang, Yiwei Lyu, Xiang Fan +9
Learning multimodal representations involves integrating information from multiple heterogeneous sources of data. It is a challenging yet crucial area with numerous real-world appl…
Cross-Modal Generalization: Learning in Low Resource Modalities via Meta-Alignment
Paul Pu Liang, Peter Wu, Liu Ziyin +2
The natural world is abundant with concepts expressed via visual, acoustic, tactile, and linguistic modalities. Much of the existing progress in multimodal learning, however, focus…
Automatically Identifying Language Family from Acoustic Examples in Low Resource Scenarios
Peter Wu, Yifan Zhong, Alan W Black
Existing multilingual speech NLP works focus on a relatively small subset of languages, and thus current linguistic understanding of languages predominantly stems from classical ap…
LEAF: A Benchmark for Federated Settings
Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu +5
Modern federated networks, such as those comprised of wearable devices, mobile phones, or autonomous vehicles, generate massive amounts of data each day. This wealth of data can he…
Machine Learning for Exam Triage
Xinyu Guan, Jessica Lee, Peter Wu +1
In this project, we extend the state-of-the-art CheXNet (Rajpurkar et al. [2017]) by making use of the additional non-image features in the dataset. Our model produced better AUROC…