240 citations · 581 across the 16 of their papers we have counts for
18 papers
Sharing Low Rank Conformer Weights for Tiny Always-On Ambient Speech Recognition Models
Steven M. Hernandez, Ding Zhao, Shaojin Ding +5
Continued improvements in machine learning techniques offer exciting new opportunities through the use of larger models and larger training datasets. However, there is a growing ne…
A Unified Cascaded Encoder ASR Model for Dynamic Model Sizes
Shaojin Ding, Weiran Wang, Ding Zhao +11
In this paper, we propose a dynamic cascaded encoder Automatic Speech Recognition (ASR) model, which unifies models for different deployment scenarios. Moreover, the model can sign…
Personal VAD 2.0: Optimizing Personal Voice Activity Detection for On-Device Speech Recognition
Shaojin Ding, Rajeev Rikhye, Qiao Liang +5
Personalization of on-device speech recognition (ASR) has seen explosive growth in recent years, largely due to the increasing popularity of personal assistant features on mobile d…
Closing the Gap between Single-User and Multi-User VoiceFilter-Lite
Rajeev Rikhye, Quan Wang, Qiao Liang +2
VoiceFilter-Lite is a speaker-conditioned voice separation model that plays a crucial role in improving speech recognition and speaker verification by suppressing overlapping speec…
Multi-user VoiceFilter-Lite via Attentive Speaker Embedding
Rajeev Rikhye, Quan Wang, Qiao Liang +2
In this paper, we propose a solution to allow speaker conditioned speech models, such as VoiceFilter-Lite, to support an arbitrary number of enrolled users in a single pass. This i…
Multi-Task Learning for End-to-End ASR Word and Utterance Confidence with Deletion Prediction
David Qiu, Yanzhang He, Qiujia Li +3
Confidence scores are very useful for downstream applications of automatic speech recognition (ASR) systems. Recent works have proposed using neural networks to learn word or utter…
Personalized Keyphrase Detection using Speaker and Environment Information
Rajeev Rikhye, Quan Wang, Qiao Liang +6
In this paper, we introduce a streaming keyphrase detection system that can be easily customized to accurately detect any phrase composed of words from a large vocabulary. The syst…
Learning Word-Level Confidence For Subword End-to-End ASR
David Qiu, Qiujia Li, Yanzhang He +9
We study the problem of word-level confidence estimation in subword-based end-to-end (E2E) models for automatic speech recognition (ASR). Although prior works have proposed trainin…
Analyzing the Quality and Stability of a Streaming End-to-End On-Device Speech Recognizer
Yuan Shangguan, Kate Knister, Yanzhang He +2
The demand for fast and accurate incremental speech recognition increases as the applications of automatic speech recognition (ASR) proliferate. Incremental speech recognizers outp…
A Streaming On-Device End-to-End Model Surpassing Server-Side Conventional Model Quality and Latency
Tara N. Sainath, Yanzhang He, Bo Li +26
Thus far, end-to-end (E2E) models have not been shown to outperform state-of-the-art conventional models with respect to both quality, i.e., word error rate (WER), and latency, i.e…
Optimizing Speech Recognition For The Edge
Yuan Shangguan, Jian Li, Qiao Liang +2
While most deployed speech recognition systems today still run on servers, we are in the midst of a transition towards deployments on edge devices. This leap to the edge is powered…
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…
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…
Streaming End-to-end Speech Recognition For Mobile Devices
Yanzhang He, Tara N. Sainath, Rohit Prabhavalkar +17
End-to-end (E2E) models, which directly predict output character sequences given input speech, are good candidates for on-device speech recognition. E2E models, however, present nu…
Streaming Small-Footprint Keyword Spotting using Sequence-to-Sequence Models
Yanzhang He, Rohit Prabhavalkar, Kanishka Rao +3
We develop streaming keyword spotting systems using a recurrent neural network transducer (RNN-T) model: an all-neural, end-to-end trained, sequence-to-sequence model which jointly…
On the Compression of Recurrent Neural Networks with an Application to LVCSR acoustic modeling for Embedded Speech Recognition
Rohit Prabhavalkar, Ouais Alsharif, Antoine Bruguier +1
We study the problem of compressing recurrent neural networks (RNNs). In particular, we focus on the compression of RNN acoustic models, which are motivated by the goal of building…
Personalized Speech recognition on mobile devices
Ian McGraw, Rohit Prabhavalkar, Raziel Alvarez +8
We describe a large vocabulary speech recognition system that is accurate, has low latency, and yet has a small enough memory and computational footprint to run faster than real-ti…
Residual Belief Propagation: Informed Scheduling for Asynchronous Message Passing
Gal Elidan, Ian McGraw, Daphne Koller
Inference for probabilistic graphical models is still very much a practical challenge in large domains. The commonly used and effective belief propagation (BP) algorithm and its ge…