RNNs for Speech
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This document summarizes and compares several techniques for improving RNN performance for speech recognition: 1) FastGRNN proposes techniques like low-rank matrix approximation and quantization to make GRUs faster and smaller. 2) LightGRU removes the reset gate from GRUs and replaces tanh with ReLU for improved speech recognition performance. 3) AWD-LSTM incorporates techniques like dropout, averaged SGD, and activation regularization to prevent overfitting in LSTMs. Overall the document evaluates different approaches for making RNNs more efficient and effective for speech tasks.
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RNNs for Speech
- 1. RNNs for Speech Faster and smaller RNNs with new regularization techniques.
- 2. Old Good RNNs Cannot train RNN!! Gradients get crazy!! Fishes are better at remembering!!! I watched Schmidhuber and liked him!! I dont care baseline, I use what the cool boys use!! Why so big, Occams will cry!! My GPU has 4GB!! I cant wait months to train!! X et al. said GRUs are better!!
- 3. What else? I need a RNN size model with LSTM performance !! I need a smaller model or a better smart phone !! FastGRNN http://manikvarma.org/pubs/kusupati18.pdf This forget gate makes no sense!! May the ReLU be with you!! I do speech recognition!! I watched Bengio and liked him!! LightGRU https://arxiv.org/abs/1803.10225 I need Regularization!!! Dropout is not good!!! AWD-LSTM https://arxiv.org/abs/1708.02182
- 4. Fast GRNN 2 trainable matrices vs 6 trainable matrices in a GRU layer. Low rank approximation of matrices: w = w1(w2).T Integer quantization for parameters. Piecewise linear approximation of non-linearities.
- 7. Light Gated Recurrent Units Remove the reset gate. Replace tanh with ReLU Batch normalization to reduce ReLU unstability. Specifically targeting speech recognition. Orthogonal weight initialization, Variational dropout
- 8. Redundancy of Reset Gate
- 9. Results 40 log-mel filter banks Maximum likelihood linear regression
- 10. All together GRU FastGRNN LightGRU
- 11. ASGD Weight Dropped LSTM Drop Connect Averaged SGD Embedding Dropout Activation Regularization
- 12. Weight Dropping Apply Drop-Connect to hidden to hidden connections. (All U matrices) Preventing recurrent unit overfitting. It needs not to modify optimized RNN implementations in DL frameworks. Apply the same dropout mask for the all sequence.
- 13. Average SGD and NT-ASGD Number of steps to start averaging Weights optimized per iterationWeights used as the final model PyTorch implementation: https://github.com/pytorch/pytorch/blob/cd9b27231b51633e76e28b6a34002ab83b0660fc/torch/optim/asgd.py NT-ASGD: Only use ASGD when validation metric fails to improve
- 14. Embedding Dropout Apply dropout in word level, that is dropout zeros-out randomly selected word vectors. Activation Regularization Panalize network for producing large changes in hidden states and large outputs leading to overfitting.
- 15. Results