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09/19/2023
Semi-Autoregressive Streaming ASR With Label Context
Non-autoregressive (NAR) modeling has gained significant interest in spe...
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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...
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05/08/2023
High-Dimensional Smoothed Entropy Estimation via Dimensionality Reduction
We study the problem of overcoming exponential sample complexity in diff...
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10/07/2022
C2KD: Cross-Lingual Cross-Modal Knowledge Distillation for Multilingual Text-Video Retrieval
Multilingual text-video retrieval methods have improved significantly in...
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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...
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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 ...
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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) ...
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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 ...
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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 ...
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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...
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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...
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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...
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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 ...
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12/08/2021
Everything at Once – Multi-modal Fusion Transformer for Video Retrieval
Multi-modal learning from video data has seen increased attention recent...
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11/08/2021
Cascaded Multilingual Audio-Visual Learning from Videos
In this paper, we explore self-supervised audio-visual models that learn...
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10/21/2021
Asynchronous Decentralized Distributed Training of Acoustic Models
Large-scale distributed training of deep acoustic models plays an import...
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08/27/2021
4-bit Quantization of LSTM-based Speech Recognition Models
We investigate the impact of aggressive low-precision representations of...
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08/24/2021
Reducing Exposure Bias in Training Recurrent Neural Network Transducers
When recurrent neural network transducers (RNNTs) are trained using the ...
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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...
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06/29/2021
Representation based meta-learning for few-shot spoken intent recognition
Spoken intent detection has become a popular approach to interface with ...
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05/03/2021
On the limit of English conversational speech recognition
In our previous work we demonstrated that a single headed attention enco...
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04/26/2021
Multimodal Clustering Networks for Self-supervised Learning from Unlabeled Videos
Multimodal self-supervised learning is getting more and more attention a...
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04/08/2021
RNN Transducer Models For Spoken Language Understanding
We present a comprehensive study on building and adapting RNN transducer...
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03/17/2021
Advancing RNN Transducer Technology for Speech Recognition
We investigate a set of techniques for RNN Transducers (RNN-Ts) that wer...
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02/08/2021
Federated Acoustic Modeling For Automatic Speech Recognition
Data privacy and protection is a crucial issue for any automatic speech ...
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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 ...
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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...
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09/30/2020
End-to-End Spoken Language Understanding Without Full Transcripts
An essential component of spoken language understanding (SLU) is slot fi...
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06/16/2020
AVLnet: Learning Audio-Visual Language Representations from Instructional Videos
Current methods for learning visually grounded language from videos ofte...
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02/04/2020
Improving Efficiency in Large-Scale Decentralized Distributed Training
Decentralized Parallel SGD (D-PSGD) and its asynchronous variant Asynchr...
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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...
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08/09/2019
Challenging the Boundaries of Speech Recognition: The MALACH Corpus
There has been huge progress in speech recognition over the last several...
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07/10/2019
A Highly Efficient Distributed Deep Learning System For Automatic Speech Recognition
Modern Automatic Speech Recognition (ASR) systems rely on distributed de...
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04/30/2019
English Broadcast News Speech Recognition by Humans and Machines
With recent advances in deep learning, considerable attention has been g...
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04/10/2019
Distributed Deep Learning Strategies For Automatic Speech Recognition
In this paper, we propose and investigate a variety of distributed deep ...
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11/30/2018
Understanding Unequal Gender Classification Accuracy from Face Images
Recent work shows unequal performance of commercial face classification ...
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10/12/2018
Estimating Information Flow in Neural Networks
We study the flow of information and the evolution of internal represent...
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06/24/2018
Beyond Backprop: Alternating Minimization with co-Activation Memory
We propose a novel online algorithm for training deep feedforward neural...
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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...
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01/13/2017
End-to-End ASR-free Keyword Search from Speech
End-to-end (E2E) systems have achieved competitive results compared to c...
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01/13/2017
Kernel Approximation Methods for Speech Recognition
We study large-scale kernel methods for acoustic modeling in speech reco...
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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...
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09/29/2015
Very Deep Multilingual Convolutional Neural Networks for LVCSR
Convolutional neural networks (CNNs) are a standard component of many cu...
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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...
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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...
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09/05/2013
