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Grid sentence is commonly used for studying the Lombard effect andNormal-to-Lombard conversion. However, it's unclear if Normal-to-Lombard modelstrained on grid sentences are sufficient for improving natural speechintelligibility in real-world applications. This paper presents the recordingof a parallel Lombard corpus (called Lombard Chinese TIMIT, LCT) extractingnatural sentences from Chinese TIMIT. Then We compare natural and gridsentences in terms of Lombard effect and Normal-to-Lombard conversion using LCTand Enhanced MAndarin Lombard Grid corpus (EMALG). Through a parametricanalysis of the Lombard effect, We find that as the noise level increases, bothnatural sentences and grid sentences exhibit similar changes in parameters, butin terms of the increase of the alpha ratio, grid sentences show a greaterincrease. Following a subjective intelligibility assessment across genders andSignal-to-Noise Ratios, the StarGAN model trained on EMALG consistentlyoutperforms the model trained on LCT in terms of improving intelligibility.This superior performance may be attributed to EMALG's larger alpha ratioincrease from normal to Lombard speech.

A comparative study of Grid and Natural sentences effects on Normal-to-Lombard conversion