Multi-task Recurrent Model for Speech and Speaker Recognition
Abstract
Although highly correlated, speech and speaker recognition have been regarded as two independent tasks and studied by two communities. This is certainly not the way that people behave: we decipher both speech content and speaker traits at the same time. This paper presents a unified model to perform speech and speaker recognition simultaneously and altogether. The model is based on a unified neural network where the output of one task is fed to the input of the other, leading to a multi-task recurrent network. Experiments show that the joint model outperforms the task-specific models on both the two tasks.
- Publication:
-
arXiv e-prints
- Pub Date:
- March 2016
- DOI:
- 10.48550/arXiv.1603.09643
- arXiv:
- arXiv:1603.09643
- Bibcode:
- 2016arXiv160309643T
- Keywords:
-
- Computer Science - Computation and Language;
- Computer Science - Machine Learning;
- Computer Science - Neural and Evolutionary Computing;
- Statistics - Machine Learning
- E-Print:
- APSIPA 2016