Search Results for author: Duc Le

Found 32 papers, 5 papers with code

Modality Confidence Aware Training for Robust End-to-End Spoken Language Understanding

no code implementations22 Jul 2023 Suyoun Kim, Akshat Shrivastava, Duc Le, Ju Lin, Ozlem Kalinli, Michael L. Seltzer

End-to-end (E2E) spoken language understanding (SLU) systems that generate a semantic parse from speech have become more promising recently.

speech-recognition Speech Recognition +1

Improving Fast-slow Encoder based Transducer with Streaming Deliberation

no code implementations15 Dec 2022 Ke Li, Jay Mahadeokar, Jinxi Guo, Yangyang Shi, Gil Keren, Ozlem Kalinli, Michael L. Seltzer, Duc Le

Experiments on Librispeech and in-house data show relative WER reductions (WERRs) from 3% to 5% with a slight increase in model size and negligible extra token emission latency compared with fast-slow encoder based transducer.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Massively Multilingual ASR on 70 Languages: Tokenization, Architecture, and Generalization Capabilities

no code implementations10 Nov 2022 Andros Tjandra, Nayan Singhal, David Zhang, Ozlem Kalinli, Abdelrahman Mohamed, Duc Le, Michael L. Seltzer

Later, we use our optimal tokenization strategy to train multiple embedding and output model to further improve our result.

Factorized Blank Thresholding for Improved Runtime Efficiency of Neural Transducers

no code implementations2 Nov 2022 Duc Le, Frank Seide, Yuhao Wang, Yang Li, Kjell Schubert, Ozlem Kalinli, Michael L. Seltzer

We show how factoring the RNN-T's output distribution can significantly reduce the computation cost and power consumption for on-device ASR inference with no loss in accuracy.

Joint Audio/Text Training for Transformer Rescorer of Streaming Speech Recognition

no code implementations31 Oct 2022 Suyoun Kim, Ke Li, Lucas Kabela, Rongqing Huang, Jiedan Zhu, Ozlem Kalinli, Duc Le

In this work, we present our Joint Audio/Text training method for Transformer Rescorer, to leverage unpaired text-only data which is relatively cheaper than paired audio-text data.

speech-recognition Speech Recognition

Robust Singular Values based on L1-norm PCA

no code implementations21 Oct 2022 Duc Le, Panos P. Markopoulos

The L2-norm (sum of squared values) formulation of PCA promotes peripheral data points and, thus, makes PCA sensitive against outliers.

Image Compression

Multimodality Multi-Lead ECG Arrhythmia Classification using Self-Supervised Learning

1 code implementation30 Sep 2022 Thinh Phan, Duc Le, Patel Brijesh, Donald Adjeroh, Jingxian Wu, Morten Olgaard Jensen, Ngan Le

Electrocardiogram (ECG) signal is one of the most effective sources of information mainly employed for the diagnosis and prediction of cardiovascular diseases (CVDs) connected with the abnormalities in heart rhythm.

ECG Classification Self-Knowledge Distillation +3

STOP: A dataset for Spoken Task Oriented Semantic Parsing

1 code implementation29 Jun 2022 Paden Tomasello, Akshat Shrivastava, Daniel Lazar, Po-chun Hsu, Duc Le, Adithya Sagar, Ali Elkahky, Jade Copet, Wei-Ning Hsu, Yossi Adi, Robin Algayres, Tu Ahn Nguyen, Emmanuel Dupoux, Luke Zettlemoyer, Abdelrahman Mohamed

Furthermore, in addition to the human-recorded audio, we are releasing a TTS-generated version to benchmark the performance for low-resource domain adaptation of end-to-end SLU systems.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +4

An Investigation of Monotonic Transducers for Large-Scale Automatic Speech Recognition

no code implementations19 Apr 2022 Niko Moritz, Frank Seide, Duc Le, Jay Mahadeokar, Christian Fuegen

The two most popular loss functions for streaming end-to-end automatic speech recognition (ASR) are RNN-Transducer (RNN-T) and connectionist temporal classification (CTC).

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Deliberation Model for On-Device Spoken Language Understanding

no code implementations4 Apr 2022 Duc Le, Akshat Shrivastava, Paden Tomasello, Suyoun Kim, Aleksandr Livshits, Ozlem Kalinli, Michael L. Seltzer

We propose a novel deliberation-based approach to end-to-end (E2E) spoken language understanding (SLU), where a streaming automatic speech recognition (ASR) model produces the first-pass hypothesis and a second-pass natural language understanding (NLU) component generates the semantic parse by conditioning on both ASR's text and audio embeddings.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

Neural-FST Class Language Model for End-to-End Speech Recognition

no code implementations28 Jan 2022 Antoine Bruguier, Duc Le, Rohit Prabhavalkar, Dangna Li, Zhe Liu, Bo wang, Eun Chang, Fuchun Peng, Ozlem Kalinli, Michael L. Seltzer

We propose Neural-FST Class Language Model (NFCLM) for end-to-end speech recognition, a novel method that combines neural network language models (NNLMs) and finite state transducers (FSTs) in a mathematically consistent framework.

Language Modelling speech-recognition +1

Scaling ASR Improves Zero and Few Shot Learning

no code implementations10 Nov 2021 Alex Xiao, Weiyi Zheng, Gil Keren, Duc Le, Frank Zhang, Christian Fuegen, Ozlem Kalinli, Yatharth Saraf, Abdelrahman Mohamed

With 4. 5 million hours of English speech from 10 different sources across 120 countries and models of up to 10 billion parameters, we explore the frontiers of scale for automatic speech recognition.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Dissecting User-Perceived Latency of On-Device E2E Speech Recognition

no code implementations6 Apr 2021 Yuan Shangguan, Rohit Prabhavalkar, Hang Su, Jay Mahadeokar, Yangyang Shi, Jiatong Zhou, Chunyang Wu, Duc Le, Ozlem Kalinli, Christian Fuegen, Michael L. Seltzer

As speech-enabled devices such as smartphones and smart speakers become increasingly ubiquitous, there is growing interest in building automatic speech recognition (ASR) systems that can run directly on-device; end-to-end (E2E) speech recognition models such as recurrent neural network transducers and their variants have recently emerged as prime candidates for this task.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Dynamic Encoder Transducer: A Flexible Solution For Trading Off Accuracy For Latency

no code implementations5 Apr 2021 Yangyang Shi, Varun Nagaraja, Chunyang Wu, Jay Mahadeokar, Duc Le, Rohit Prabhavalkar, Alex Xiao, Ching-Feng Yeh, Julian Chan, Christian Fuegen, Ozlem Kalinli, Michael L. Seltzer

DET gets similar accuracy as a baseline model with better latency on a large in-house data set by assigning a lightweight encoder for the beginning part of one utterance and a full-size encoder for the rest.

speech-recognition Speech Recognition

Deep Shallow Fusion for RNN-T Personalization

no code implementations16 Nov 2020 Duc Le, Gil Keren, Julian Chan, Jay Mahadeokar, Christian Fuegen, Michael L. Seltzer

End-to-end models in general, and Recurrent Neural Network Transducer (RNN-T) in particular, have gained significant traction in the automatic speech recognition community in the last few years due to their simplicity, compactness, and excellent performance on generic transcription tasks.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Improving RNN Transducer Based ASR with Auxiliary Tasks

1 code implementation5 Nov 2020 Chunxi Liu, Frank Zhang, Duc Le, Suyoun Kim, Yatharth Saraf, Geoffrey Zweig

End-to-end automatic speech recognition (ASR) models with a single neural network have recently demonstrated state-of-the-art results compared to conventional hybrid speech recognizers.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Alignment Restricted Streaming Recurrent Neural Network Transducer

no code implementations5 Nov 2020 Jay Mahadeokar, Yuan Shangguan, Duc Le, Gil Keren, Hang Su, Thong Le, Ching-Feng Yeh, Christian Fuegen, Michael L. Seltzer

There is a growing interest in the speech community in developing Recurrent Neural Network Transducer (RNN-T) models for automatic speech recognition (ASR) applications.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Improved Neural Language Model Fusion for Streaming Recurrent Neural Network Transducer

no code implementations26 Oct 2020 Suyoun Kim, Yuan Shangguan, Jay Mahadeokar, Antoine Bruguier, Christian Fuegen, Michael L. Seltzer, Duc Le

Recurrent Neural Network Transducer (RNN-T), like most end-to-end speech recognition model architectures, has an implicit neural network language model (NNLM) and cannot easily leverage unpaired text data during training.

Language Modelling speech-recognition +1

Classification of Huntington Disease using Acoustic and Lexical Features

no code implementations7 Aug 2020 Matthew Perez, Wenyu Jin, Duc Le, Noelle Carlozzi, Praveen Dayalu, Angela Roberts, Emily Mower Provost

Speech is a critical biomarker for Huntington Disease (HD), with changes in speech increasing in severity as the disease progresses.

Classification General Classification

Weak-Attention Suppression For Transformer Based Speech Recognition

no code implementations18 May 2020 Yangyang Shi, Yongqiang Wang, Chunyang Wu, Christian Fuegen, Frank Zhang, Duc Le, Ching-Feng Yeh, Michael L. Seltzer

Transformers, originally proposed for natural language processing (NLP) tasks, have recently achieved great success in automatic speech recognition (ASR).

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

G2G: TTS-Driven Pronunciation Learning for Graphemic Hybrid ASR

no code implementations22 Oct 2019 Duc Le, Thilo Koehler, Christian Fuegen, Michael L. Seltzer

Grapheme-based acoustic modeling has recently been shown to outperform phoneme-based approaches in both hybrid and end-to-end automatic speech recognition (ASR), even on non-phonemic languages like English.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

From Senones to Chenones: Tied Context-Dependent Graphemes for Hybrid Speech Recognition

no code implementations2 Oct 2019 Duc Le, Xiaohui Zhang, Weiyi Zheng, Christian Fügen, Geoffrey Zweig, Michael L. Seltzer

There is an implicit assumption that traditional hybrid approaches for automatic speech recognition (ASR) cannot directly model graphemes and need to rely on phonetic lexicons to get competitive performance, especially on English which has poor grapheme-phoneme correspondence.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

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