  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.
