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Found 14 papers, 3 papers with code
A Unified Cascaded Encoder ASR Model for Dynamic Model Sizes
In this paper, we propose a dynamic cascaded encoder Automatic Speech Recognition (ASR) model, which unifies models for different deployment scenarios.
Personal VAD 2.0: Optimizing Personal Voice Activity Detection for On-Device Speech Recognition
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 devices and smart home speakers.
Closing the Gap between Single-User and Multi-User VoiceFilter-Lite
However, one limitation of VoiceFilter-Lite, and other speaker-conditioned speech models in general, is that these models are usually limited to a single target speaker.
Multi-user VoiceFilter-Lite via Attentive Speaker Embedding
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.
Automatic Speech Recognition
Text-Independent Speaker Verification
Personalized Keyphrase Detection using Speaker and Environment Information
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.
JST-RR Model: Joint Modeling of Ratings and Reviews in Sentiment-Topic Prediction
In this work, we propose a probabilistic model to accommodate both textual reviews and overall ratings with consideration of their intrinsic connection for a joint sentiment-topic prediction.
A Better and Faster End-to-End Model for Streaming ASR
To address this, we explore replacing the LSTM layers in the encoder of our E2E model with Conformer layers [4], which has shown good improvements for ASR.
Audio and Speech Processing Sound
Dynamic Sparsity Neural Networks for Automatic Speech Recognition
In automatic speech recognition (ASR), model pruning is a widely adopted technique that reduces model size and latency to deploy neural network models on edge devices with resource constraints.
A Streaming On-Device End-to-End Model Surpassing Server-Side Conventional Model Quality and Latency
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., the time the hypothesis is finalized after the user stops speaking.
Optimizing Speech Recognition For The Edge
While most deployed speech recognition systems today still run on servers, we are in the midst of a transition towards deployments on edge devices.
Two-Pass End-to-End Speech Recognition
However, this model still lags behind a large state-of-the-art conventional model in quality [2].
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models.
Streaming End-to-end Speech Recognition For Mobile Devices
End-to-end (E2E) models, which directly predict output character sequences given input speech, are good candidates for on-device speech recognition.
To Know Where We Are: Vision-Based Positioning in Outdoor Environments
Augmented reality (AR) displays become more and more popular recently, because of its high intuitiveness for humans and high-quality head-mounted display have rapidly developed.
