A comparative study of Grid and Natural sentences effects on
  Normal-to-Lombard conversion
Hongyang Chen Yuhong Yang Qingmu Liu Baifeng Li Weiping Tu Song Lin
Grid sentence is commonly used for studying the Lombard effect and
Normal-to-Lombard conversion. However, it's unclear if Normal-to-Lombard models
trained on grid sentences are sufficient for improving natural speech
intelligibility in real-world applications. This paper presents the recording
of a parallel Lombard corpus (called Lombard Chinese TIMIT, LCT) extracting
natural sentences from Chinese TIMIT. Then We compare natural and grid
sentences in terms of Lombard effect and Normal-to-Lombard conversion using LCT
and Enhanced MAndarin Lombard Grid corpus (EMALG). Through a parametric
analysis of the Lombard effect, We find that as the noise level increases, both
natural sentences and grid sentences exhibit similar changes in parameters, but
in terms of the increase of the alpha ratio, grid sentences show a greater
increase. Following a subjective intelligibility assessment across genders and
Signal-to-Noise Ratios, the StarGAN model trained on EMALG consistently
outperforms the model trained on LCT in terms of improving intelligibility.
This superior performance may be attributed to EMALG's larger alpha ratio
increase from normal to Lombard speech.