TITLE: Common Phone: A Multilingual Dataset for Robust Acoustic Modelling 

ABSTRACT: Current state of the art acoustic models can easily comprise more than 100
million parameters. This growing complexity demands larger training datasets to
maintain a decent generalization of the final decision function. An ideal
dataset is not necessarily large in size, but large with respect to the amount
of unique speakers, utilized hardware and varying recording conditions. This
enables a machine learning model to explore as much of the domain-specific
input space as possible during parameter estimation. This work introduces
Common Phone, a gender-balanced, multilingual corpus recorded from more than
11.000 contributors via Mozilla's Common Voice project. It comprises around 116
hours of speech enriched with automatically generated phonetic segmentation. A
Wav2Vec 2.0 acoustic model was trained with the Common Phone to perform
phonetic symbol recognition and validate the quality of the generated phonetic
annotation. The architecture achieved a PER of 18.1 % on the entire test set,
computed with all 101 unique phonetic symbols, showing slight differences
between the individual languages. We conclude that Common Phone provides
sufficient variability and reliable phonetic annotation to help bridging the
gap between research and application of acoustic models.
Current state of the art acoustic models can easily comprise more than 100
million parameters. This growing complexity demands larger training datasets to
maintain a decent generalization of the final decision function. An ideal
dataset is not necessarily large in size, but large with respect to the amount
of unique speakers, utilized hardware and varying recording conditions. This
enables a machine learning model to explore as much of the domain-specific
input space as possible during parameter estimation. This work introduces
Common Phone, a gender-balanced, multilingual corpus recorded from more than
11.000 contributors via Mozilla's Common Voice project. It comprises around 116
hours of speech enriched with automatically generated phonetic segmentation. A
Wav2Vec 2.0 acoustic model was trained with the Common Phone to perform
phonetic symbol recognition and validate the quality of the generated phonetic
annotation. The architecture achieved a PER of 18.1 % on the entire test set,
computed with all 101 unique phonetic symbols, showing slight differences
between the individual languages. We conclude that Common Phone provides
sufficient variability and reliable phonetic annotation to help bridging the
gap between research and application of acoustic models.
Common Phone: A Multilingual Dataset for Robust Acoustic Modelling
http://arxiv.org/abs/2201.05912v2