Common Phone: A Multilingual Dataset for Robust Acoustic Modelling
This provides a resource to improve acoustic model robustness for speech processing applications, though it is incremental as it builds on existing datasets and methods.
The authors tackled the need for diverse training data for acoustic models by introducing Common Phone, a gender-balanced multilingual speech dataset with 116 hours of audio and automatic phonetic segmentation, achieving a phone error rate (PER) of 18.1% on a test set with 101 phonetic symbols.
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.