Adaptation of Deep Neural Network Acoustic Models for Robust Automatic Speech Recognition
By Khe Chai Sim, Yanmin Qian, G. Mantena, Lahiru Samarakoon, Souvik Kundu, Tian Tan. 2017
14 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Bo Li
- Jinyu Li
- Tara N. Sainath
- Khe Chai Sim
- Arun Narayanan
- Dong Yu
- Rohit Prabhavalkar
- Michiel Bacchiani
In Signal Processing
- Bo Li
- Tara N. Sainath
- Jinyu Li
- Khe Chai Sim
- Dong Yu
- Arun Narayanan
- Michiel Bacchiani
- Rohit Prabhavalkar
Citations in the corpus
- Personalization of End-to-End Speech Recognition on Mobile Devices for Named Entities (2019, background): “There are existing speaker adaptation techniques for neural networks that offer a more compact model representation by having a large number of global parameters shared across different speakers and a smaller number of speaker-specific parameters [13, 14, 15, 16, 17, 18, 19, 20].”
- An Investigation Into On-device Personalization of End-to-end Automatic Speech Recognition Models (2019, unknown): “A common solution to this problem is to adapt a well-trained model using a small amount of user-specific data to build a personalized model for each user [4, 5, 6, 7, 8, 9, 10, 11, 12, 13].”
- Domain Adaptation Using Factorized Hidden Layer for Robust Automatic Speech Recognition (2018, background): “A wide range of DNN adaptation techniques for robust ASR are described in [4, 5].”