# On the Relationship Between Short-Time Objective Intelligibility and Short-Time Spectral-Amplitude Mean-Square Error for Speech Enhancement

arXiv: https://arxiv.org/abs/1806.08404 · HTML: https://mlsanity.com/paper/1806.08404/

## Key facts

- Cited by 3 papers in the map (data through 19 Oct 2026).
- First posted on arXiv 21 Jun 2018.

## Built on by (most cited first, 3 of 3)

- [Attention-based Speech Enhancement Using Human Quality Perception Modelling](https://mlsanity.com/paper/2303.13685/) (2023)
- [Complex Recurrent Variational Autoencoder with Application to Speech Enhancement](https://mlsanity.com/paper/2204.02195/) (2022)
- [A Multiscale Autoencoder (MSAE) Framework for End-to-End Neural Network Speech Enhancement](https://mlsanity.com/paper/2309.12121/) (2023)

## Abstract (arXiv)

The majority of deep neural network (DNN) based speech enhancement algorithms rely on the mean-square error (MSE) criterion of short-time spectral amplitudes (STSA), which has no apparent link to human perception, e.g. speech intelligibility. Short-Time Objective Intelligibility (STOI), a popular state-of-the-art speech intelligibility estimator, on the other hand, relies on linear correlation of speech temporal envelopes. This raises the question if a DNN training criterion based on envelope linear correlation (ELC) can lead to improved speech intelligibility performance of DNN based speech enhancement algorithms compared to algorithms based on the STSA-MSE criterion. In this paper we derive that, under certain general conditions, the STSA-MSE and ELC criteria are practically equivalent, and we provide empirical data to support our theoretical results. Furthermore, our experimental findings suggest that the standard STSA minimum-MSE estimator is near optimal, if the objective is to enhance noisy speech in a manner which is optimal with respect to the STOI speech intelligibility estimator.