b'Karen Livescu'

research

∙ 12/20/2022

SLUE Phase-2: A Benchmark Suite of Diverse Spoken Language Understanding Tasks

Spoken language understanding (SLU) tasks have been studied for many dec...

0 Suwon Shon, et al. ∙

research

∙ 12/16/2022

Context-aware Fine-tuning of Self-supervised Speech Models

Self-supervised pre-trained transformers have improved the state of the ...

0 Suwon Shon, et al. ∙

research

∙ 11/08/2022

Comparative layer-wise analysis of self-supervised speech models

Many self-supervised speech models, varying in their pre-training object...

0 Ankita Pasad, et al. ∙

research

∙ 05/25/2022

Open-Domain Sign Language Translation Learned from Online Video

Existing work on sign language translation–that is, translation from sig...

0 Bowen Shi, et al. ∙

research

∙ 05/21/2022

Self-Supervised Speech Representation Learning: A Review

Although supervised deep learning has revolutionized speech and audio pr...

0 Abdelrahman Mohamed, et al. ∙

research

∙ 03/24/2022

Searching for fingerspelled content in American Sign Language

Natural language processing for sign language video - including tasks li...

0 Bowen Shi, et al. ∙

research

∙ 12/14/2021

On the Use of External Data for Spoken Named Entity Recognition

Spoken language understanding (SLU) tasks involve mapping from speech au...

4 Ankita Pasad, et al. ∙

research

∙ 11/19/2021

SLUE: New Benchmark Tasks for Spoken Language Understanding Evaluation on Natural Speech

Progress in speech processing has been facilitated by shared datasets an...

21 Suwon Shon, et al. ∙

research

∙ 10/16/2021

Substructure Distribution Projection for Zero-Shot Cross-Lingual Dependency Parsing

We present substructure distribution projection (SubDP), a technique tha...

0 Haoyue Shi, et al. ∙

research

∙ 09/20/2021

On Generalization in Coreference Resolution

While coreference resolution is defined independently of dataset domain,...

5 Shubham Toshniwal, et al. ∙

research

∙ 07/10/2021

Layer-wise Analysis of a Self-supervised Speech Representation Model

Recently proposed self-supervised learning approaches have been successf...

4 Ankita Pasad, et al. ∙

research

∙ 04/03/2021

Fingerspelling Detection in American Sign Language

Fingerspelling, in which words are signed letter by letter, is an import...

1 Bowen Shi, et al. ∙

research

∙ 02/26/2021

Learning Chess Blindfolded: Evaluating Language Models on State Tracking

Transformer language models have made tremendous strides in natural lang...

5 Shubham Toshniwal, et al. ∙

research

∙ 01/02/2021

Substructure Substitution: Structured Data Augmentation for NLP

We study a family of data augmentation methods, substructure substitutio...

0 Haoyue Shi, et al. ∙

research

∙ 12/03/2020

A Correspondence Variational Autoencoder for Unsupervised Acoustic Word Embeddings

We propose a new unsupervised model for mapping a variable-duration spee...

0 Puyuan Peng, et al. ∙

research

∙ 11/24/2020

Acoustic span embeddings for multilingual query-by-example search

Query-by-example (QbE) speech search is the task of matching spoken quer...

0 Yushi Hu, et al. ∙

research

∙ 10/06/2020

Learning to Ignore: Long Document Coreference with Bounded Memory Neural Networks

Long document coreference resolution remains a challenging task due to t...

0 Shubham Toshniwal, et al. ∙

research

∙ 10/06/2020

On the Role of Supervision in Unsupervised Constituency Parsing

We analyze several recent unsupervised constituency parsing models, whic...

0 Haoyue Shi, et al. ∙

research

∙ 07/01/2020

Whole-Word Segmental Speech Recognition with Acoustic Word Embeddings

Segmental models are sequence prediction models in which scores of hypot...

0 Bowen Shi, et al. ∙

research

∙ 06/24/2020

Multilingual Jointly Trained Acoustic and Written Word Embeddings

Acoustic word embeddings (AWEs) are vector representations of spoken wor...

0 Yushi Hu, et al. ∙

research

∙ 06/11/2020

Discrete Latent Variable Representations for Low-Resource Text Classification

While much work on deep latent variable models of text uses continuous l...

0 Shuning Jin, et al. ∙

research

∙ 06/06/2020

A Cross-Task Analysis of Text Span Representations

Many natural language processing (NLP) tasks involve reasoning with text...

0 Shubham Toshniwal, et al. ∙

research

∙ 05/06/2020

PeTra: A Sparsely Supervised Memory Model for People Tracking

We propose PeTra, a memory-augmented neural network designed to track en...

0 Shubham Toshniwal, et al. ∙

research

∙ 01/28/2020

Unsupervised Pre-training of Bidirectional Speech Encoders via Masked Reconstruction

We propose an approach for pre-training speech representations via a mas...

0 Weiran Wang, et al. ∙

research

∙ 08/28/2019

Fingerspelling recognition in the wild with iterative visual attention

Sign language recognition is a challenging gesture sequence recognition ...

10 Bowen Shi, et al. ∙

research

∙ 06/23/2019

Variational Sequential Labelers for Semi-Supervised Learning

We introduce a family of multitask variational methods for semi-supervis...

0 Mingda Chen, et al. ∙

research

∙ 06/07/2019

Visually Grounded Neural Syntax Acquisition

We present the Visually Grounded Neural Syntax Learner (VG-NSL), an appr...

2 Haoyue Shi, et al. ∙

research

∙ 04/24/2019

On the Contributions of Visual and Textual Supervision in Low-resource Semantic Speech Retrieval

Recent work has shown that speech paired with images can be used to lear...

0 Ankita Pasad, et al. ∙

research

∙ 04/15/2019

Semantic query-by-example speech search using visual grounding

A number of recent studies have started to investigate how speech system...

0 Herman Kamper, et al. ∙

research

∙ 03/29/2019

Acoustically Grounded Word Embeddings for Improved Acoustics-to-Word Speech Recognition

Direct acoustics-to-word (A2W) systems for end-to-end automatic speech r...

0 Shane Settle, et al. ∙

research

∙ 10/26/2018

American Sign Language fingerspelling recognition in the wild

We address the problem of American Sign Language fingerspelling recognit...

0 Bowen Shi, et al. ∙

research

∙ 09/05/2018

Pre-training on high-resource speech recognition improves low-resource speech-to-text translation

We present a simple approach to improve direct speech-to-text translatio...

0 Sameer Bansal, et al. ∙

research

∙ 07/27/2018

A Comparison of Techniques for Language Model Integration in Encoder-Decoder Speech Recognition

Attention-based recurrent neural encoder-decoder models present an elega...

0 Shubham Toshniwal, et al. ∙

research

∙ 07/17/2018

Hierarchical Multitask Learning for CTC-based Speech Recognition

Previous work has shown that neural encoder-decoder speech recognition c...

0 Kalpesh Krishna, et al. ∙

research

∙ 03/24/2018

Low-Resource Speech-to-Text Translation

Speech-to-text translation has many potential applications for low-resou...

0 Sameer Bansal, et al. ∙

research

∙ 03/19/2018

Acoustic feature learning using cross-domain articulatory measurements

Previous work has shown that it is possible to improve speech recognitio...

0 Qingming Tang, et al. ∙

research

∙ 03/19/2018

Acoustic feature learning cross-domain articulatory measurements

Previous work has shown that it is possible to improve speech recognitio...

0 Qingming Tang, et al. ∙

research

∙ 10/28/2017

A Study of All-Convolutional Encoders for Connectionist Temporal Classification

Connectionist temporal classification (CTC) is a popular sequence predic...

0 Kalpesh Krishna, et al. ∙

research

∙ 10/09/2017

Multitask training with unlabeled data for end-to-end sign language fingerspelling recognition

We address the problem of automatic American Sign Language fingerspellin...

0 Bowen Shi, et al. ∙

research

∙ 10/05/2017

Semantic keyword spotting by learning from images and speech

We consider the problem of representing semantic concepts in speech by l...

0 Herman Kamper, et al. ∙

research

∙ 08/11/2017

Acoustic Feature Learning via Deep Variational Canonical Correlation Analysis

We study the problem of acoustic feature learning in the setting where w...

0 Qingming Tang, et al. ∙

research

∙ 08/01/2017

End-to-End Neural Segmental Models for Speech Recognition

Segmental models are an alternative to frame-based models for sequence p...

0 Hao Tang, et al. ∙

research

∙ 06/12/2017

Query-by-Example Search with Discriminative Neural Acoustic Word Embeddings

Query-by-example search often uses dynamic time warping (DTW) for compar...

0 Shane Settle, et al. ∙

research

∙ 06/09/2017

Learning to Embed Words in Context for Syntactic Tasks

We present models for embedding words in the context of surrounding word...

0 Lifu Tu, et al. ∙

research

∙ 04/24/2017

Joint Modeling of Text and Acoustic-Prosodic Cues for Neural Parsing

In conversational speech, the acoustic signal provides cues that help li...

0 Trang Tran, et al. ∙

research

∙ 04/05/2017

Multitask Learning with Low-Level Auxiliary Tasks for Encoder-Decoder Based Speech Recognition

End-to-end training of deep learning-based models allows for implicit le...

0 Shubham Toshniwal, et al. ∙

research

∙ 03/23/2017

Visually grounded learning of keyword prediction from untranscribed speech

During language acquisition, infants have the benefit of visual cues to ...

0 Herman Kamper, et al. ∙

research

∙ 03/23/2017

An embedded segmental K-means model for unsupervised segmentation and clustering of speech

Unsupervised segmentation and clustering of unlabelled speech are core p...

0 Herman Kamper, et al. ∙

research

∙ 11/14/2016

Multi-view Recurrent Neural Acoustic Word Embeddings

Recent work has begun exploring neural acoustic word embeddings---fixed-...

0 Wanjia He, et al. ∙

research

∙ 11/08/2016

Discriminative Acoustic Word Embeddings: Recurrent Neural Network-Based Approaches

Acoustic word embeddings --- fixed-dimensional vector representations of...

0 Shane Settle, et al. ∙

Karen Livescu

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