Month: April 2022
[Note] Conformer: Convolution-augmented Transformer for Speech Recognition
- SOTA performance on Librispeech
- A novel way to combine CNN + Transformer
- to model both local(CNN) and global(Self-attention) dependencies
- Conformer
- 4 modules: 0.5 FFN + MHSA + CNN + 0.5 FFN
- Macaron-style half-step resudual FFN
- placing CNN after MHSA is more effective
- swish activation led to faster convergence
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[Note] InterAug: Augmenting Noisy Intermediate Predictions for CTC-based ASR
https://arxiv.org/abs/2204.00174
- A novel training method for CTC-based ASR using augmented intermediate representations for conditioning
- a extension of self-condition CTC
- Methods: noisy conditioning
- feature space: Mask time or feature
- token space: Insert, delete, substitute token in “condition”.
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