On Multi-Domain Training and Adaptation of End-to-End RNN Acoustic Models for Distant Speech Recognition

By Seyedmahdad Mirsamadi, John H. L. Hansen. 2017

22 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Speech & Audio, 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

1. Ahmet Emin Bulut
2. Shahram Ghorbani
3. John H. L. Hansen

In Speech & Audio

1. Ahmet Emin Bulut
2. Shahram Ghorbani
3. John H. L. Hansen

In Signal Processing

1. Ahmet Emin Bulut
2. Shahram Ghorbani
3. John H. L. Hansen

Citations in the corpus

• Toward Domain-Invariant Speech Recognition via Large Scale Training (2018, neutral): “In [11], a model is adapted to a previously unseen far-field condition by learning a linear transform of an intermediate layer, where the noise or the domain information is best encoded.”
• Advancing Multi-Accented Lstm-CTC Speech Recognition Using a Domain Specific Student-Teacher Learning Paradigm (2018, methodology): “Given training data of multiple conditions, we need to exploit the data efficiently to train an improved multi-condition (domain) ASR model [5, 6, 7, 8].”