Brian Kingsbury

research

∙ 09/19/2023

Semi-Autoregressive Streaming ASR With Label Context

Non-autoregressive (NAR) modeling has gained significant interest in spe...

0 Siddhant Arora, et al. ∙

research

∙ 05/21/2023

Comparison of Multilingual Self-Supervised and Weakly-Supervised Speech Pre-Training for Adaptation to Unseen Languages

Recent models such as XLS-R and Whisper have made multilingual speech te...

0 Andrew Rouditchenko, et al. ∙

research

∙ 05/08/2023

High-Dimensional Smoothed Entropy Estimation via Dimensionality Reduction

We study the problem of overcoming exponential sample complexity in diff...

0 Kristjan Greenewald, et al. ∙

research

∙ 10/07/2022

C2KD: Cross-Lingual Cross-Modal Knowledge Distillation for Multilingual Text-Video Retrieval

Multilingual text-video retrieval methods have improved significantly in...

0 Andrew Rouditchenko, et al. ∙

research

∙ 08/03/2022

VQ-T: RNN Transducers using Vector-Quantized Prediction Network States

Beam search, which is the dominant ASR decoding algorithm for end-to-end...

0 Jiatong Shi, et al. ∙

research

∙ 06/16/2022

Accelerating Inference and Language Model Fusion of Recurrent Neural Network Transducers via End-to-End 4-bit Quantization

We report on aggressive quantization strategies that greatly accelerate ...

0 Andrea Fasoli, et al. ∙

research

∙ 04/11/2022

Tokenwise Contrastive Pretraining for Finer Speech-to-BERT Alignment in End-to-End Speech-to-Intent Systems

Recent advances in End-to-End (E2E) Spoken Language Understanding (SLU) ...

0 Vishal Sunder, et al. ∙

research

∙ 04/11/2022

Towards End-to-End Integration of Dialog History for Improved Spoken Language Understanding

Dialog history plays an important role in spoken language understanding ...

7 Vishal Sunder, et al. ∙

research

∙ 03/29/2022

Improving Generalization of Deep Neural Network Acoustic Models with Length Perturbation and N-best Based Label Smoothing

We introduce two techniques, length perturbation and n-best based label ...

0 Xiaodong Cui, et al. ∙

research

∙ 02/26/2022

Towards Reducing the Need for Speech Training Data To Build Spoken Language Understanding Systems

The lack of speech data annotated with labels required for spoken langua...

0 Samuel Thomas, et al. ∙

research

∙ 02/26/2022

Integrating Text Inputs For Training and Adapting RNN Transducer ASR Models

Compared to hybrid automatic speech recognition (ASR) systems that use a...

0 Samuel Thomas, et al. ∙

research

∙ 02/21/2022

A new data augmentation method for intent classification enhancement and its application on spoken conversation datasets

Intent classifiers are vital to the successful operation of virtual agen...

0 Zvi Kons, et al. ∙

research

∙ 01/28/2022

Improving End-to-End Models for Set Prediction in Spoken Language Understanding

The goal of spoken language understanding (SLU) systems is to determine ...

0 Hong-Kwang J. Kuo, et al. ∙

research

∙ 12/08/2021

Everything at Once – Multi-modal Fusion Transformer for Video Retrieval

Multi-modal learning from video data has seen increased attention recent...

0 Nina Shvetsova, et al. ∙

research

∙ 11/08/2021

Cascaded Multilingual Audio-Visual Learning from Videos

In this paper, we explore self-supervised audio-visual models that learn...

0 Andrew Rouditchenko, et al. ∙

research

∙ 10/21/2021

Asynchronous Decentralized Distributed Training of Acoustic Models

Large-scale distributed training of deep acoustic models plays an import...

9 Xiaodong Cui, et al. ∙

research

∙ 08/27/2021

4-bit Quantization of LSTM-based Speech Recognition Models

We investigate the impact of aggressive low-precision representations of...

0 Andrea Fasoli, et al. ∙

research

∙ 08/24/2021

Reducing Exposure Bias in Training Recurrent Neural Network Transducers

When recurrent neural network transducers (RNNTs) are trained using the ...

0 Xiaodong Cui, et al. ∙

research

∙ 08/18/2021

Integrating Dialog History into End-to-End Spoken Language Understanding Systems

End-to-end spoken language understanding (SLU) systems that process huma...

13 Jatin Ganhotra, et al. ∙

research

∙ 06/29/2021

Representation based meta-learning for few-shot spoken intent recognition

Spoken intent detection has become a popular approach to interface with ...

4 Ashish Mittal, et al. ∙

research

∙ 05/03/2021

On the limit of English conversational speech recognition

In our previous work we demonstrated that a single headed attention enco...

0 Zoltán Tüske, et al. ∙

research

∙ 04/26/2021

Multimodal Clustering Networks for Self-supervised Learning from Unlabeled Videos

Multimodal self-supervised learning is getting more and more attention a...

0 Brian Chen, et al. ∙

research

∙ 04/08/2021

RNN Transducer Models For Spoken Language Understanding

We present a comprehensive study on building and adapting RNN transducer...

0 Samuel Thomas, et al. ∙

research

∙ 03/17/2021

Advancing RNN Transducer Technology for Speech Recognition

We investigate a set of techniques for RNN Transducers (RNN-Ts) that wer...

0 George Saon, et al. ∙

research

∙ 02/08/2021

Federated Acoustic Modeling For Automatic Speech Recognition

Data privacy and protection is a crucial issue for any automatic speech ...

0 Xiaodong Cui, et al. ∙

research

∙ 11/16/2020

End-to-end spoken language understanding using transformer networks and self-supervised pre-trained features

Transformer networks and self-supervised pre-training have consistently ...

0 Edmilson Morais, et al. ∙

research

∙ 10/08/2020

Leveraging Unpaired Text Data for Training End-to-End Speech-to-Intent Systems

Training an end-to-end (E2E) neural network speech-to-intent (S2I) syste...

0 Yinghui Huang, et al. ∙

research

∙ 09/30/2020

End-to-End Spoken Language Understanding Without Full Transcripts

An essential component of spoken language understanding (SLU) is slot fi...

0 Hong-Kwang J. Kuo, et al. ∙

research

∙ 06/16/2020

AVLnet: Learning Audio-Visual Language Representations from Instructional Videos

Current methods for learning visually grounded language from videos ofte...

14 Andrew Rouditchenko, et al. ∙

research

∙ 02/04/2020

Improving Efficiency in Large-Scale Decentralized Distributed Training

Decentralized Parallel SGD (D-PSGD) and its asynchronous variant Asynchr...

16 Wei Zhang, et al. ∙

research

∙ 01/20/2020

Single headed attention based sequence-to-sequence model for state-of-the-art results on Switchboard-300

It is generally believed that direct sequence-to-sequence (seq2seq) spee...

0 Zoltán Tüske, et al. ∙

research

∙ 08/09/2019

Challenging the Boundaries of Speech Recognition: The MALACH Corpus

There has been huge progress in speech recognition over the last several...

0 Michael Picheny, et al. ∙

research

∙ 07/10/2019

A Highly Efficient Distributed Deep Learning System For Automatic Speech Recognition

Modern Automatic Speech Recognition (ASR) systems rely on distributed de...

8 Wei Zhang, et al. ∙

research

∙ 04/30/2019

English Broadcast News Speech Recognition by Humans and Machines

With recent advances in deep learning, considerable attention has been g...

0 Samuel Thomas, et al. ∙

research

∙ 04/10/2019

Distributed Deep Learning Strategies For Automatic Speech Recognition

In this paper, we propose and investigate a variety of distributed deep ...

0 Wei Zhang, et al. ∙

research

∙ 11/30/2018

Understanding Unequal Gender Classification Accuracy from Face Images

Recent work shows unequal performance of commercial face classification ...

0 Vidya Muthukumar, et al. ∙

research

∙ 10/12/2018

Estimating Information Flow in Neural Networks

We study the flow of information and the evolution of internal represent...

0 Ziv Goldfeld, et al. ∙

research

∙ 06/24/2018

Beyond Backprop: Alternating Minimization with co-Activation Memory

We propose a novel online algorithm for training deep feedforward neural...

0 Anna Choromanska, et al. ∙

research

∙ 12/08/2017

Building competitive direct acoustics-to-word models for English conversational speech recognition

Direct acoustics-to-word (A2W) models in the end-to-end paradigm have re...

0 Kartik Audhkhasi, et al. ∙

research

∙ 01/13/2017

End-to-End ASR-free Keyword Search from Speech

End-to-end (E2E) systems have achieved competitive results compared to c...

0 Kartik Audhkhasi, et al. ∙

research

∙ 01/13/2017

Kernel Approximation Methods for Speech Recognition

We study large-scale kernel methods for acoustic modeling in speech reco...

0 Avner May, et al. ∙

research

∙ 03/18/2016

A Comparison between Deep Neural Nets and Kernel Acoustic Models for Speech Recognition

We study large-scale kernel methods for acoustic modeling and compare to...

0 Zhiyun Lu, et al. ∙

research

∙ 09/29/2015

Very Deep Multilingual Convolutional Neural Networks for LVCSR

Convolutional neural networks (CNNs) are a standard component of many cu...

0 Tom Sercu, et al. ∙

research

∙ 11/14/2014

How to Scale Up Kernel Methods to Be As Good As Deep Neural Nets

The computational complexity of kernel methods has often been a major ba...

0 Zhiyun Lu, et al. ∙

research

∙ 09/05/2013

Accelerating Hessian-free optimization for deep neural networks by implicit preconditioning and sampling

Hessian-free training has become a popular parallel second or- der optim...

0 Tara N. Sainath, et al. ∙

research

∙ 09/05/2013

Improvements to deep convolutional neural networks for LVCSR

Deep Convolutional Neural Networks (CNNs) are more powerful than Deep Ne...

0 Tara N. Sainath, et al. ∙

Brian Kingsbury

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