# Multi-task Recurrent Model for Speech and Speaker Recognition

arXiv: https://arxiv.org/abs/1603.09643 · HTML: https://mlsanity.com/paper/1603.09643/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2016.

## Built on by (most cited first, 2 of 2)

- [SpeechNet: A Universal Modularized Model for Speech Processing Tasks](https://mlsanity.com/paper/2105.03070/) (2021)
- [TIMIT Speaker Profiling: A Comparison of Multi-task learning and Single-task learning Approaches](https://mlsanity.com/paper/2404.12077/) (2024)

## Abstract (arXiv)

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.