Grant P. Strimel | DeepAI

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113 publications
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∙ 05/12/2023

Accelerator-Aware Training for Transducer-Based Speech Recognition

Machine learning model weights and activations are represented in full-p...

0 Suhaila M. Shakiah, et al. ∙

∙ 05/09/2023

Robust Acoustic and Semantic Contextual Biasing in Neural Transducers for Speech Recognition

Attention-based contextual biasing approaches have shown significant imp...

0 Xuandi Fu, et al. ∙

∙ 04/03/2023

Dual-Attention Neural Transducers for Efficient Wake Word Spotting in Speech Recognition

We present dual-attention neural biasing, an architecture designed to bo...

0 Saumya Y. Sahai, et al. ∙

∙ 03/31/2023

Dialog act guided contextual adapter for personalized speech recognition

Personalization in multi-turn dialogs has been a long standing challenge...

0 Feng-Ju Chang, et al. ∙

∙ 10/17/2022

Sub-8-bit quantization for on-device speech recognition: a regularization-free approach

For on-device automatic speech recognition (ASR), quantization aware tra...

0 Kai Zhen, et al. ∙

∙ 09/29/2022

ConvRNN-T: Convolutional Augmented Recurrent Neural Network Transducers for Streaming Speech Recognition

The recurrent neural network transducer (RNN-T) is a prominent streaming...

0 Martin Radfar, et al. ∙

∙ 07/05/2022

Compute Cost Amortized Transformer for Streaming ASR

We present a streaming, Transformer-based end-to-end automatic speech re...

0 Yi Xie, et al. ∙

∙ 06/15/2022

Latency Control for Keyword Spotting

Conversational agents commonly utilize keyword spotting (KWS) to initiat...

0 Christin Jose, et al. ∙

∙ 05/26/2022

Contextual Adapters for Personalized Speech Recognition in Neural Transducers

Personal rare word recognition in end-to-end Automatic Speech Recognitio...

0 Kanthashree Mysore Sathyendra, et al. ∙

∙ 05/11/2022

A neural prosody encoder for end-ro-end dialogue act classification

Dialogue act classification (DAC) is a critical task for spoken language...

0 Kai Wei, et al. ∙

∙ 04/01/2022

Multi-task RNN-T with Semantic Decoder for Streamable Spoken Language Understanding

End-to-end Spoken Language Understanding (E2E SLU) has attracted increas...

0 Xuandi Fu, et al. ∙

∙ 12/13/2021

Attentive Contextual Carryover for Multi-Turn End-to-End Spoken Language Understanding

Recent years have seen significant advances in end-to-end (E2E) spoken l...

0 Kai Wei, et al. ∙

∙ 08/03/2021

Bifocal Neural ASR: Exploiting Keyword Spotting for Inference Optimization

We present Bifocal RNN-T, a new variant of the Recurrent Neural Network ...

0 Jonathan Macoskey, et al. ∙

∙ 08/03/2021

Learning a Neural Diff for Speech Models

As more speech processing applications execute locally on edge devices, ...

0 Jonathan Macoskey, et al. ∙

∙ 08/03/2021

Amortized Neural Networks for Low-Latency Speech Recognition

We introduce Amortized Neural Networks (AmNets), a compute cost- and lat...

0 Jonathan Macoskey, et al. ∙

∙ 06/14/2021

CoDERT: Distilling Encoder Representations with Co-learning for Transducer-based Speech Recognition

We propose a simple yet effective method to compress an RNN-Transducer (...

0 Rupak Vignesh Swaminathan, et al. ∙

∙ 08/06/2020

Semantic Complexity in End-to-End Spoken Language Understanding

End-to-end spoken language understanding (SLU) models are a class of mod...

0 Joseph P. McKenna, et al. ∙

∙ 07/19/2018

Statistical Model Compression for Small-Footprint Natural Language Understanding

In this paper we investigate statistical model compression applied to na...

0 Grant P. Strimel, et al. ∙