Residual Excitation Skewness for Automatic Speech Polarity Detection

TL;DR

This paper introduces a simple, robust, and computationally efficient method for automatic speech polarity detection based on excitation skewness, outperforming existing methods across various conditions.

Contribution

The paper presents a novel, straightforward algorithm using excitation skewness for speech polarity detection, demonstrating superior accuracy and robustness over state-of-the-art approaches.

Findings

Error rate of 0.06% in clean conditions

Outperforms four state-of-the-art methods

Highly robust in noisy and reverberant environments

Abstract

Detecting the correct speech polarity is a necessary step prior to several speech processing techniques. An error on its determination could have a dramatic detrimental impact on their performance. As current systems have to deal with increasing amounts of data stemming from multiple devices, the automatic detection of speech polarity has become a crucial problem. For this purpose, we here propose a very simple algorithm based on the skewness of two excitation signals. The method is shown on 10 speech corpora (8545 files) to lead to an error rate of only 0.06% in clean conditions and to clearly outperform four state-of-the-art methods. Besides it significantly reduces the computational load through its simplicity and is observed to exhibit the strongest robustness in both noisy and reverberant environments.