Kalman Filter for Speech Denoising

Hi, I have been trying to denoise a speech signal using Kalman Filter. I have use the following code for my calculation. May I know if I am in the right direction? Many literature that I have read uses a state space model for the Kalman Filter? How can I do that for a corrupted speech signal? System Identification?
Thanks.
def my_calculation(y_corrupt):
sum1 = 0
square1 = 0
mean1 = 0
E_estimated = 0.00001
E_measured = 0.00001
noise_model= np.zeros((len(y_corrupt)))
y_filtered = np.zeros((len(y_corrupt)))
N = len(y_corrupt)
for i in range (2,N):
KG = E_estimated/(E_estimated+E_measured)
#KG = KG**2
noise_model[i] = noise_model[i-1] + KG*(y_corrupt[i]-noise_model[i-1])
E_estimated= (1-KG)*E_measured;
#E_estimated= (1-KG)*E_estimated;
square1 = square1 + noise_model[i]**2
sum1 = sum1 + noise_model[i]
mean1 = (mean1*(i-1)+noise_model[i])/i
E_measured = square1/i - mean1**2
y_filtered[i] = y_corrupt[i] - noise_model[i]
return y_filtered

I suggest you see the notebook https://github.com/strongio/torch-kalman for a general application. You can customize it for your data.