PMMSD: Development of the Matrix Sentence Intelligibility Dataset for Mandarin with Lombard Effect

Hanchen Pei; Yuhong Yang; Xufeng Chen; Qingmu Liu; Hongyang Chen; Weiping Tu; Song Lin

ICASSP 2023accepted0 citations

PMMSD: Development of the Matrix Sentence Intelligibility Dataset for Mandarin with Lombard Effect

Hanchen Pei, Yuhong Yang, Xufeng Chen, Qingmu Liu, Hongyang Chen, Weiping Tu, Song Lin

Abstract

This paper presents a Paired Mandarin Matrix Sentence Dataset (PMMSD), which will be available after publication. PMMSD is the first Mandarin matrix sentence intelligibility dataset containing both plain and Lombard speech for scientific research. The results verify that different Lombard styles would affect word intelligibility to different degrees and the Lombard effect helps maintain homogeneous intelligibility against contextual interference. All of the discoveries indicate that the Lombard effect should be considered when building intelligibility datasets with noise in the future.

BibTeX

@inproceedings{icassp2023_pmmsddevelopment,
  title = {PMMSD: Development of the Matrix Sentence Intelligibility Dataset for Mandarin with Lombard Effect},
  author = {Hanchen Pei and Yuhong Yang and Xufeng Chen and Qingmu Liu and Hongyang Chen and Weiping Tu and Song Lin},
  booktitle = {ICASSP 2023},
  year = {2023}
}