Mandarin Lombard Flavor Classification

TL;DR

This study classifies Mandarin Lombard speech into distinct categories based on noise type and decibel levels, revealing how speech adjustments vary with environmental noise to improve intelligibility.

Contribution

It introduces a novel flavor classification method for Mandarin Lombard speech, analyzing effects of noise type and level on speech modulation.

Findings

Four distinct speech categories identified across noise conditions.

Transition points vary with noise type and decibel level.

Both SSN and babble noise influence speech adjustments.

Abstract

The Lombard effect refers to individuals' unconscious modulation of vocal effort in response to variations in the ambient noise levels, intending to enhance speech intelligibility. The impact of different decibel levels and types of background noise on Lombard effects remains unclear. Building upon the characteristic of Lombard speech that individuals adjust their speech to improve intelligibility dynamically based on the self-feedback speech, we propose a flavor classification approach for the Lombard effect. We first collected Mandarin Lombard speech under different noise conditions, then simulated self-feedback speech, and ultimately conducted the statistical test on the word correct rate. We found that both SSN and babble noise types result in four distinct categories of Mandarin Lombard speech in the range of 30 to 80 dBA with different transition points.