Before revision:  This includes a frequency-weighted SpecAugment policy and a frequency-divided CNN feature extractor for better capturing the high frequency structures of whispered speech, and a layer-wise transfer learning approach to pre-train a model with normal speech then fine-tuning it with whispered speech to bridge the gap between whispered and normal speech.
After revision:  This includes a frequency-weighted SpecAugment policy and a frequency-divided CNN feature extractor for better capturing the high frequency structures of whispered speech, and a layer-wise transfer learning approach to pre-train a model with normal or normal-to-whispered converted speech then fine-tune it with whispered speech to bridge the gap between whispered and normal speech.