3.6k citations · 4.5k across the 15 of their papers we have counts for
16 papers
IndicVoices: Towards building an Inclusive Multilingual Speech Dataset for Indian Languages
Tahir Javed, Janki Atul Nawale, Eldho Ittan George +18
We present INDICVOICES, a dataset of natural and spontaneous speech containing a total of 7348 hours of read (9%), extempore (74%) and conversational (17%) audio from 16237 speaker…
Discovering Adaptable Symbolic Algorithms from Scratch
Stephen Kelly, Daniel S. Park, Xingyou Song +8
Autonomous robots deployed in the real world will need control policies that rapidly adapt to environmental changes. To this end, we propose AutoRobotics-Zero (ARZ), a method based…
Large-scale Language Model Rescoring on Long-form Data
Tongzhou Chen, Cyril Allauzen, Yinghui Huang +8
In this work, we study the impact of Large-scale Language Models (LLM) on Automated Speech Recognition (ASR) of YouTube videos, which we use as a source for long-form ASR. We demon…
Noise2Music: Text-conditioned Music Generation with Diffusion Models
Qingqing Huang, Daniel S. Park, Tao Wang +12
We introduce Noise2Music, where a series of diffusion models is trained to generate high-quality 30-second music clips from text prompts. Two types of diffusion models, a generator…
Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages
Yu Zhang, Wei Han, James Qin +24
We introduce the Universal Speech Model (USM), a single large model that performs automatic speech recognition (ASR) across 100+ languages. This is achieved by pre-training the enc…
G-Augment: Searching for the Meta-Structure of Data Augmentation Policies for ASR
Gary Wang, Ekin D. Cubuk, Andrew Rosenberg +6
Data augmentation is a ubiquitous technique used to provide robustness to automatic speech recognition (ASR) training. However, even as so much of the ASR training process has beco…
Universal Paralinguistic Speech Representations Using Self-Supervised Conformers
Joel Shor, Aren Jansen, Wei Han +2
Many speech applications require understanding aspects beyond the words being spoken, such as recognizing emotion, detecting whether the speaker is wearing a mask, or distinguishin…
BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition
Yu Zhang, Daniel S. Park, Wei Han +23
We summarize the results of a host of efforts using giant automatic speech recognition (ASR) models pre-trained using large, diverse unlabeled datasets containing approximately a m…
SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Network
William Chan, Daniel Park, Chris Lee +3
We present SpeechStew, a speech recognition model that is trained on a combination of various publicly available speech recognition datasets: AMI, Broadcast News, Common Voice, Lib…
Efficient Knowledge Distillation for RNN-Transducer Models
Sankaran Panchapagesan, Daniel S. Park, Chung-Cheng Chiu +3
Knowledge Distillation is an effective method of transferring knowledge from a large model to a smaller model. Distillation can be viewed as a type of model compression, and has pl…
Towards NNGP-guided Neural Architecture Search
Daniel S. Park, Jaehoon Lee, Daiyi Peng +2
The predictions of wide Bayesian neural networks are described by a Gaussian process, known as the Neural Network Gaussian Process (NNGP). Analytic forms for NNGP kernels are known…
Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition
Yu Zhang, James Qin, Daniel S. Park +5
We employ a combination of recent developments in semi-supervised learning for automatic speech recognition to obtain state-of-the-art results on LibriSpeech utilizing the unlabele…
Improved Noisy Student Training for Automatic Speech Recognition
Daniel S. Park, Yu Zhang, Ye Jia +5
Recently, a semi-supervised learning method known as "noisy student training" has been shown to improve image classification performance of deep networks significantly. Noisy stude…
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…
The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study
Daniel S. Park, Jascha Sohl-Dickstein, Quoc V. Le +1
We investigate how the final parameters found by stochastic gradient descent are influenced by over-parameterization. We generate families of models by increasing the number of cha…
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…