def minDistance(features, codebooks):
speaker = 0
distmin = np.inf
for k in range(np.shape(codebooks)[0]):
D = EUDistance(features, codebooks[k, :, :])
dist = np.sum(np.min(D, axis=1)) / (np.shape(D)[0])
if dist < distmin:
distmin = dist
speaker = k
return speaker
def distance_min(feature, codebook):
distmini = np.inf
speakernum = 0
for l in range(np.shape(codebook)[0]):
Dis = EUDistance(feature, codebook[l, :, :])
dista = np.sum(np.min(Dis, axis=1)) / (np.shape(Dis)[0])
if dista < distmini:
speakernum = l
distmini = dista
return speakernum