3.6k citations · 5.2k across the 39 of their papers we have counts for
8 papers · 1 filter
SpecAugment on Large Scale Datasets
Daniel S. Park, Yu Zhang, Chung-Cheng Chiu +5
Recently, SpecAugment, an augmentation scheme for automatic speech recognition that acts directly on the spectrogram of input utterances, has shown to be highly effective in enhanc…
A comparison of end-to-end models for long-form speech recognition
Chung-Cheng Chiu, Wei Han, Yu Zhang +11
End-to-end automatic speech recognition (ASR) models, including both attention-based models and the recurrent neural network transducer (RNN-T), have shown superior performance com…
Speech Sentiment Analysis via Pre-trained Features from End-to-end ASR Models
Zhiyun Lu, Liangliang Cao, Yu Zhang +2
In this paper, we propose to use pre-trained features from end-to-end ASR models to solve speech sentiment analysis as a down-stream task. We show that end-to-end ASR features, whi…
Recognizing long-form speech using streaming end-to-end models
Arun Narayanan, Rohit Prabhavalkar, Chung-Cheng Chiu +3
All-neural end-to-end (E2E) automatic speech recognition (ASR) systems that use a single neural network to transduce audio to word sequences have been shown to achieve state-of-the…
Two-Pass End-to-End Speech Recognition
Tara N. Sainath, Ruoming Pang, David Rybach +9
The requirements for many applications of state-of-the-art speech recognition systems include not only low word error rate (WER) but also low latency. Specifically, for many use-ca…
Monotonic Infinite Lookback Attention for Simultaneous Machine Translation
Naveen Arivazhagan, Colin Cherry, Wolfgang Macherey +5
Simultaneous machine translation begins to translate each source sentence before the source speaker is finished speaking, with applications to live and streaming scenarios. Simulta…
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
Daniel S. Park, William Chan, Yu Zhang +4
We present SpecAugment, a simple data augmentation method for speech recognition. SpecAugment is applied directly to the feature inputs of a neural network (i.e., filter bank coeff…
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Jonathan Shen, Patrick Nguyen, Yonghui Wu +88
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models. Lingvo models a…