FuzzyGCP: A deep learning architecture for automatic spoken language identification from speech signals

作者：

Highlights：

• Developed an automatic spoken language identification system called FuzzyGCP.

• Deep Dumb MLP is used as a classifier for numeric features.

• Image based features are fed to Deep CNN and Semi-supervised GAN models.

• Formed an ensemble by uniting results of 3 models using a fuzzy integral measure.

• Evaluated the model on 4 standard publicly available multi-lingual datasets.

摘要

•Developed an automatic spoken language identification system called FuzzyGCP.•Deep Dumb MLP is used as a classifier for numeric features.•Image based features are fed to Deep CNN and Semi-supervised GAN models.•Formed an ensemble by uniting results of 3 models using a fuzzy integral measure.•Evaluated the model on 4 standard publicly available multi-lingual datasets.

论文关键词：Spoken language identification,Speech signal,Deep learning,GAN,DNN,MLP,Ensemble learning,Choquet integral,Spectrogram

论文评审过程：Received 3 June 2020, Revised 18 October 2020, Accepted 28 November 2020, Available online 8 December 2020, Version of Record 9 December 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.114416