Online incremental adaptation of deep neural networks using auxiliary Gaussian mixture models in speech recognition
US9466292B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | May 3, 2013 |
| Grant date | Oct 11, 2016 |
| Priority date | — |
| Expiry date | Oct 3, 2034 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG10L15/14
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods and systems for online incremental adaptation of neural networks using Gaussian mixture models in speech recognition are described. In an example, a computing device may be configured to receive an audio signal and a subsequent audio signal, both signals having speech content. The computing device may be configured to apply a speaker-specific feature transform to the audio signal to obtain a transformed audio signal. The speaker-specific feature transform may be configured to include speaker-specific speech characteristics of a speaker-profile relating to the speech content. Further, the computing device may be configured to process the transformed audio signal using a neural network trained to estimate a respective speech content of the audio signal. Based on outputs of the neural network, the computing device may be configured to modify the speaker-specific feature transform, and apply the modified speaker-specific feature transform to a subsequent audio signal.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.