# DNN-based uncertainty estimation for weighted DNN-HMM ASR

## TL;DR

This paper introduces a DNN-based method to estimate uncertainty in noisy speech observations, enhancing the robustness of DNN-HMM speech recognition systems across various noise conditions.

## Contribution

It proposes a novel DNN approach to estimate uncertainty directly from enhanced noisy observations, improving speech recognition accuracy under noisy environments.

## Key findings

- Improved recognition accuracy with uncertainty integration.
- Effective uncertainty estimation across multiple noise conditions.
- Enhanced robustness in multi-condition training scenarios.

## Abstract

In this paper, the uncertainty is defined as the mean square error between a given enhanced noisy observation vector and the corresponding clean one. Then, a DNN is trained by using enhanced noisy observation vectors as input and the uncertainty as output with a training database. In testing, the DNN receives an enhanced noisy observation vector and delivers the estimated uncertainty. This uncertainty in employed in combination with a weighted DNN-HMM based speech recognition system and compared with an existing estimation of the noise cancelling uncertainty variance based on an additive noise model. Experiments were carried out with Aurora-4 task. Results with clean, multi-noise and multi-condition training are presented.

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Source: https://tomesphere.com/paper/1705.10368