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Found 14 papers, 1 papers with code
Federated Domain Adaptation for ASR with Full Self-Supervision
Cross-device federated learning (FL) protects user privacy by collaboratively training a model on user devices, therefore eliminating the need for collecting, storing, and manually labeling user data.
Omni-sparsity DNN: Fast Sparsity Optimization for On-Device Streaming E2E ASR via Supernet
From wearables to powerful smart devices, modern automatic speech recognition (ASR) models run on a variety of edge devices with different computational budgets.
Streaming Transformer Transducer Based Speech Recognition Using Non-Causal Convolution
This paper improves the streaming transformer transducer for speech recognition by using non-causal convolution.
Noisy Training Improves E2E ASR for the Edge
We apply noisy training to improve both dense and sparse state-of-the-art Emformer models and observe consistent WER reduction.
Dissecting User-Perceived Latency of On-Device E2E Speech Recognition
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.
Flexi-Transducer: Optimizing Latency, Accuracy and Compute forMulti-Domain On-Device Scenarios
In order to achieve flexible and better accuracy and latency trade-offs, the following techniques are used.
Contextualized Streaming End-to-End Speech Recognition with Trie-Based Deep Biasing and Shallow Fusion
How to leverage dynamic contextual information in end-to-end speech recognition has remained an active research area.
Memory-efficient Speech Recognition on Smart Devices
Recurrent transducer models have emerged as a promising solution for speech recognition on the current and next generation smart devices.
Alignment Restricted Streaming Recurrent Neural Network Transducer
There is a growing interest in the speech community in developing Recurrent Neural Network Transducer (RNN-T) models for automatic speech recognition (ASR) applications.
Improved Neural Language Model Fusion for Streaming Recurrent Neural Network Transducer
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.
Analyzing the Quality and Stability of a Streaming End-to-End On-Device Speech Recognizer
The demand for fast and accurate incremental speech recognition increases as the applications of automatic speech recognition (ASR) proliferate.
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.
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.
