Search Results for author:
Found 6 papers, 1 papers with code
SNDCNN: Self-normalizing deep CNNs with scaled exponential linear units for speech recognition
The most popular way to train very deep CNNs is to use shortcut connections (SC) together with batch normalization (BN).
Voice trigger detection from LVCSR hypothesis lattices using bidirectional lattice recurrent neural networks
We propose a method to reduce false voice triggers of a speech-enabled personal assistant by post-processing the hypothesis lattice of a server-side large-vocabulary continuous speech recognizer (LVCSR) via a neural network.
Training Large-Vocabulary Neural Language Models by Private Federated Learning for Resource-Constrained Devices
Our goal is to train a large neural network language model (NNLM) on compute-constrained devices while preserving privacy using FL and DP.
AlerTiger: Deep Learning for AI Model Health Monitoring at LinkedIn
Data-driven companies use AI models extensively to develop products and intelligent business solutions, making the health of these models crucial for business success.
Acoustic Model Fusion for End-to-end Speech Recognition
Recent advances in deep learning and automatic speech recognition (ASR) have enabled the end-to-end (E2E) ASR system and boosted the accuracy to a new level.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Personalization of CTC-based End-to-End Speech Recognition Using Pronunciation-Driven Subword Tokenization
Recent advances in deep learning and automatic speech recognition have improved the accuracy of end-to-end speech recognition systems, but recognition of personal content such as contact names remains a challenge.
