What to do when overfitting does not improve using regularization and droppout?
It's hard to say without knowing more about the data. Some of the possible solutions can be -

Use a shallower network
Get more training data
Make sure your training and validation set comes from the same distribution
I'm testing Conv, GRU, LSTM and simple Dense and I don't get 70 to 80%
My network converges very fast and overfits in the first seasons, could it be the data?
Layer (type)                 Output Shape              Param #
lstm_46 (LSTM)               (None, 65, 32)            6272

dropout_67 (Dropout)         (None, 65, 32)            0

bidirectional_20 (Bidirectio (None, 65, 64)            16640

dropout_68 (Dropout)         (None, 65, 64)            0

lstm_48 (LSTM)               (None, 32)                12416

dropout_69 (Dropout)         (None, 32)                0

flatten_28 (Flatten)         (None, 32)                0

dense_73 (Dense)             (None, 10)                330

activation_72 (Activation)   (None, 10)                0

dense_74 (Dense)             (None, 1)                 11

activation_73 (Activation)   (None, 1)                 0
Total params: 35,669
Trainable params: 35,669
Non-trainable params: 0