2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDSPE-13.3
Paper Title CONVOLUTIONAL DROPOUT AND WORDPIECE AUGMENTATION FOR END-TO-END SPEECH RECOGNITION
Authors Hainan Xu, Yinghui Huang, Yun Zhu, Kartik Audhkhasi, Bhuvana Ramabhadran, Google, United States
SessionSPE-13: Speech Recognition 5: New Algorithms
LocationGather.Town
Session Time:Wednesday, 09 June, 13:00 - 13:45
Presentation Time:Wednesday, 09 June, 13:00 - 13:45
Presentation Poster
Topic Speech Processing: [SPE-GASR] General Topics in Speech Recognition
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Regularization and data augmentation are crucial to training end-to-end automatic speech recognition systems. Dropout is a popular regularization technique, which operates on each neuron independently by multiplying it with a Bernoulli random variable. We propose a generalization of dropout, called “convolutional dropout”, where each neuron’s activation is replaced with a randomly-weighted linear combination of neuron values in its neighborhood. We believe that this formulation combines the regularizing effect of dropout with the smoothing effects of the convolution operation. In addition to convolutional dropout, this paper also proposes using random wordpiece segmentations as a data augmentation scheme during training, inspired by results in neural machine translation. We adopt both these methods during the training of transformer-transducer speech recognition models, and show consistent WER improvements on Librispeech as well as across different languages.