def _update_beam(self, beam_state, utt_id, state):
"""Calculate log probability for shift and assignment process based on current beam state."""
loss = 0
speaker = utt_id[-4]
# Convert the state into one-hot vector
label = np.zeros(self.num_state)
label[state] = 1
# Get original ce loss
logit = np.reshape(self.all_logits[utt_id], [4,])
loss = utils.cross_entropy(label, logit)
# RESCORING:
# An existing state
if state in np.unique(beam_state.emo_sequence) and speaker in np.unique(beam_state.spk_sequence):
# Find last state
last_idx = utils.find_last_idx(beam_state.spk_sequence, speaker)
last_state = beam_state.emo_sequence[last_idx]
# No shift
if state == last_state:
loss -= np.log(1 - self.transition_bias)
# Shift
else:
loss -= np.log(self.transition_bias) + \
np.log(beam_state.block_counts[state]) - \
np.log(sum(beam_state.block_counts) + self.crp_alpha)
beam_state.block_counts[state] += 1
# A new state
else:
loss -= np.log(self.transition_bias) + \
np.log(self.crp_alpha) - \
np.log(sum(beam_state.block_counts) + \
self.crp_alpha)
beam_state.block_counts[state] += 1
new_beam_state = beam_state.update(speaker, state, loss)
return new_beam_state
def _update_beam(self, beam_state, utt_id, state):
"""Calculate log probability for shift and assignment process based on current beam state."""
loss = 0
speaker = utt_id[-4]
# Convert the state into one-hot vector
label = np.zeros(self.num_state)
label[state] = 1
# Get original ce loss
logit = np.reshape(self.all_logits[utt_id], [
4,
])
loss = utils.cross_entropy(label, logit)
# RESCORING:
# An existing state (song lin original)
if state in np.unique(
beam_state.emo_sequence) and speaker in np.unique(
beam_state.spk_sequence):
# Find last state
last_idx = utils.find_last_idx(beam_state.spk_sequence, speaker)
last_state = beam_state.emo_sequence[last_idx]
# No shift
if state == last_state:
loss -= np.log(1 - self.transition_bias)
# Shift
else:
loss -= np.log(self.transition_bias) + \
np.log(beam_state.block_counts[state]) - \
np.log(sum(beam_state.block_counts) + self.crp_alpha)
beam_state.block_counts[state] += 1
# A new state
else:
loss -= np.log(self.transition_bias) + \
np.log(self.crp_alpha) - \
np.log(sum(beam_state.block_counts) + \
self.crp_alpha)
beam_state.block_counts[state] += 1
'''
# An existing state (ML model for emotion shift)
if state in np.unique(beam_state.emo_sequence) and speaker in np.unique(beam_state.spk_sequence):
# Find last state
last_idx = utils.find_last_idx(beam_state.spk_sequence, speaker)
last_state = beam_state.emo_sequence[last_idx]
# No shift
if state == last_state:
loss -= np.log(1 - self.ML_model_emo_shift_output.get(str(utt_id)))
# Shift
else:
loss -= np.log(self.ML_model_emo_shift_output.get(str(utt_id))) + \
np.log(beam_state.block_counts[state]) - \
np.log(sum(beam_state.block_counts) + self.crp_alpha)
beam_state.block_counts[state] += 1
# A new state
else:
loss -= np.log(self.ML_model_emo_shift_output.get(str(utt_id))) + \
np.log(self.crp_alpha) - \
np.log(sum(beam_state.block_counts) + \
self.crp_alpha)
beam_state.block_counts[state] += 1
'''
'''
# CRP denominator minus last state
# Find last state of current dialog (t-1)
if len(beam_state.emo_sequence) == 0: #first utterance of this dialog
last_state_seq = None
#block_counts_last_state = 0
else:
#{ang, hap, neu, sad}
last_state_seq = beam_state.emo_sequence[len(beam_state.emo_sequence)-1]
#block_counts_last_state = beam_state.block_counts[last_state_seq]
# Find last state of current spk
last_idx = utils.find_last_idx(beam_state.spk_sequence, speaker)
if last_idx != None:
last_state = beam_state.emo_sequence[last_idx]
block_counts_last_state = beam_state.block_counts[last_state]
else:
block_counts_last_state = 0
# An existing state
if state in np.unique(beam_state.emo_sequence) and speaker in np.unique(beam_state.spk_sequence):
# No shift
if state == last_state:
loss -= np.log(1 - self.transition_bias)
# Shift
else:
loss -= np.log(self.transition_bias) + \
np.log(beam_state.block_counts[state]) - \
np.log(sum(beam_state.block_counts)-block_counts_last_state + self.crp_alpha)
beam_state.block_counts[state] += 1
# A new state
else:
loss -= np.log(self.transition_bias) + \
np.log(self.crp_alpha) - \
np.log(sum(beam_state.block_counts)-block_counts_last_state + self.crp_alpha)
beam_state.block_counts[state] += 1
'''
'''
# Find last state of this speaker (Bigram) (Intra)
last_idx = utils.find_last_idx(beam_state.spk_sequence, speaker)
if last_idx == None: #this is speaker first utterance
loss -= np.log(1)
else:
#{ang, hap, neu, sad}
last_state = beam_state.emo_sequence[last_idx]
if last_state == 0 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['a2a'])
elif last_state == 0 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['a2h'])
elif last_state == 0 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['a2n'])
elif last_state == 0 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['a2s'])
elif last_state == 1 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['h2a'])
elif last_state == 1 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['h2h'])
elif last_state == 1 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['h2n'])
elif last_state == 1 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['h2s'])
elif last_state == 2 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['n2a'])
elif last_state == 2 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['n2h'])
elif last_state == 2 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['n2n'])
elif last_state == 2 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['n2s'])
elif last_state == 3 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['s2a'])
elif last_state == 3 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['s2h'])
elif last_state == 3 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['s2n'])
elif last_state == 3 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['s2s'])
'''
'''
# Find last state of current dialog (Bigram) (Sequential)
if len(beam_state.emo_sequence) == 0: #first utterance of this dialog
loss -= np.log(1)
else:
#{ang, hap, neu, sad}
last_state = beam_state.emo_sequence[len(beam_state.emo_sequence)-1]
if last_state == 0 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['a2a'])
elif last_state == 0 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['a2h'])
elif last_state == 0 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['a2n'])
elif last_state == 0 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['a2s'])
elif last_state == 1 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['h2a'])
elif last_state == 1 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['h2h'])
elif last_state == 1 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['h2n'])
elif last_state == 1 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['h2s'])
elif last_state == 2 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['n2a'])
elif last_state == 2 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['n2h'])
elif last_state == 2 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['n2n'])
elif last_state == 2 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['n2s'])
elif last_state == 3 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['s2a'])
elif last_state == 3 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['s2h'])
elif last_state == 3 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['s2n'])
elif last_state == 3 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['s2s'])
'''
'''
# Find last and the second to last states of this speaker (Trigram) (Intra)
last_idx = utils.find_last_idx(beam_state.spk_sequence, speaker)
second_to_last_idx = utils.find_second_to_last_idx(beam_state.spk_sequence, speaker)
if last_idx == None or second_to_last_idx == None: #this is speaker first or second utterance
loss -= np.log(1)
else:
#{ang, hap, neu, sad}
last_state = beam_state.emo_sequence[last_idx]
second_to_last_state = beam_state.emo_sequence[second_to_last_idx]
if second_to_last_state == 0 and last_state == 0 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['aaa'])
elif second_to_last_state == 0 and last_state == 0 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['aah'])
elif second_to_last_state == 0 and last_state == 0 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['aan'])
elif second_to_last_state == 0 and last_state == 0 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['aas'])
elif second_to_last_state == 0 and last_state == 1 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['aha'])
elif second_to_last_state == 0 and last_state == 1 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['ahh'])
elif second_to_last_state == 0 and last_state == 1 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['ahn'])
elif second_to_last_state == 0 and last_state == 1 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['ahs'])
elif second_to_last_state == 0 and last_state == 2 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['ana'])
elif second_to_last_state == 0 and last_state == 2 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['anh'])
elif second_to_last_state == 0 and last_state == 2 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['ann'])
elif second_to_last_state == 0 and last_state == 2 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['ans'])
elif second_to_last_state == 0 and last_state == 3 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['asa'])
elif second_to_last_state == 0 and last_state == 3 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['ash'])
elif second_to_last_state == 0 and last_state == 3 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['asn'])
elif second_to_last_state == 0 and last_state == 3 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['ass'])
###################################################################
elif second_to_last_state == 1 and last_state == 0 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['haa'])
elif second_to_last_state == 1 and last_state == 0 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['hah'])
elif second_to_last_state == 1 and last_state == 0 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['han'])
elif second_to_last_state == 1 and last_state == 0 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['has'])
elif second_to_last_state == 1 and last_state == 1 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['hha'])
elif second_to_last_state == 1 and last_state == 1 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['hhh'])
elif second_to_last_state == 1 and last_state == 1 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['hhn'])
elif second_to_last_state == 1 and last_state == 1 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['hhs'])
elif second_to_last_state == 1 and last_state == 2 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['hna'])
elif second_to_last_state == 1 and last_state == 2 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['hnh'])
elif second_to_last_state == 1 and last_state == 2 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['hnn'])
elif second_to_last_state == 1 and last_state == 2 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['hns'])
elif second_to_last_state == 1 and last_state == 3 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['hsa'])
elif second_to_last_state == 1 and last_state == 3 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['hsh'])
elif second_to_last_state == 1 and last_state == 3 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['hsn'])
elif second_to_last_state == 1 and last_state == 3 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['hss'])
###################################################################
elif second_to_last_state == 2 and last_state == 0 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['naa'])
elif second_to_last_state == 2 and last_state == 0 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['nah'])
elif second_to_last_state == 2 and last_state == 0 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['nan'])
elif second_to_last_state == 2 and last_state == 0 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['nas'])
elif second_to_last_state == 2 and last_state == 1 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['nha'])
elif second_to_last_state == 2 and last_state == 1 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['nhh'])
elif second_to_last_state == 2 and last_state == 1 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['nhn'])
elif second_to_last_state == 2 and last_state == 1 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['nhs'])
elif second_to_last_state == 2 and last_state == 2 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['nna'])
elif second_to_last_state == 2 and last_state == 2 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['nnh'])
elif second_to_last_state == 2 and last_state == 2 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['nnn'])
elif second_to_last_state == 2 and last_state == 2 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['nns'])
elif second_to_last_state == 2 and last_state == 3 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['nsa'])
elif second_to_last_state == 2 and last_state == 3 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['nsh'])
elif second_to_last_state == 2 and last_state == 3 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['nsn'])
elif second_to_last_state == 2 and last_state == 3 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['nss'])
###################################################################
elif second_to_last_state == 3 and last_state == 0 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['saa'])
elif second_to_last_state == 3 and last_state == 0 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['sah'])
elif second_to_last_state == 3 and last_state == 0 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['san'])
elif second_to_last_state == 3 and last_state == 0 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['sas'])
elif second_to_last_state == 3 and last_state == 1 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['sha'])
elif second_to_last_state == 3 and last_state == 1 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['shh'])
elif second_to_last_state == 3 and last_state == 1 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['shn'])
elif second_to_last_state == 3 and last_state == 1 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['shs'])
elif second_to_last_state == 3 and last_state == 2 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['sna'])
elif second_to_last_state == 3 and last_state == 2 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['snh'])
elif second_to_last_state == 3 and last_state == 2 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['snn'])
elif second_to_last_state == 3 and last_state == 2 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['sns'])
elif second_to_last_state == 3 and last_state == 3 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['ssa'])
elif second_to_last_state == 3 and last_state == 3 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['ssh'])
elif second_to_last_state == 3 and last_state == 3 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['ssn'])
elif second_to_last_state == 3 and last_state == 3 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['sss'])
'''
'''
# Find last state of current dialog (Trigram) (Sequential)
if len(beam_state.emo_sequence) == 0 or len(beam_state.emo_sequence) == 1: #first or second utterance of this dialog
loss -= np.log(1)
else:
#{ang, hap, neu, sad}
last_state = beam_state.emo_sequence[len(beam_state.emo_sequence)-1]
second_to_last_state = beam_state.emo_sequence[len(beam_state.emo_sequence)-2]
if second_to_last_state == 0 and last_state == 0 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['aaa'])
elif second_to_last_state == 0 and last_state == 0 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['aah'])
elif second_to_last_state == 0 and last_state == 0 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['aan'])
elif second_to_last_state == 0 and last_state == 0 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['aas'])
elif second_to_last_state == 0 and last_state == 1 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['aha'])
elif second_to_last_state == 0 and last_state == 1 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['ahh'])
elif second_to_last_state == 0 and last_state == 1 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['ahn'])
elif second_to_last_state == 0 and last_state == 1 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['ahs'])
elif second_to_last_state == 0 and last_state == 2 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['ana'])
elif second_to_last_state == 0 and last_state == 2 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['anh'])
elif second_to_last_state == 0 and last_state == 2 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['ann'])
elif second_to_last_state == 0 and last_state == 2 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['ans'])
elif second_to_last_state == 0 and last_state == 3 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['asa'])
elif second_to_last_state == 0 and last_state == 3 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['ash'])
elif second_to_last_state == 0 and last_state == 3 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['asn'])
elif second_to_last_state == 0 and last_state == 3 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['ass'])
###################################################################
elif second_to_last_state == 1 and last_state == 0 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['haa'])
elif second_to_last_state == 1 and last_state == 0 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['hah'])
elif second_to_last_state == 1 and last_state == 0 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['han'])
elif second_to_last_state == 1 and last_state == 0 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['has'])
elif second_to_last_state == 1 and last_state == 1 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['hha'])
elif second_to_last_state == 1 and last_state == 1 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['hhh'])
elif second_to_last_state == 1 and last_state == 1 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['hhn'])
elif second_to_last_state == 1 and last_state == 1 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['hhs'])
elif second_to_last_state == 1 and last_state == 2 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['hna'])
elif second_to_last_state == 1 and last_state == 2 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['hnh'])
elif second_to_last_state == 1 and last_state == 2 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['hnn'])
elif second_to_last_state == 1 and last_state == 2 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['hns'])
elif second_to_last_state == 1 and last_state == 3 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['hsa'])
elif second_to_last_state == 1 and last_state == 3 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['hsh'])
elif second_to_last_state == 1 and last_state == 3 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['hsn'])
elif second_to_last_state == 1 and last_state == 3 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['hss'])
###################################################################
elif second_to_last_state == 2 and last_state == 0 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['naa'])
elif second_to_last_state == 2 and last_state == 0 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['nah'])
elif second_to_last_state == 2 and last_state == 0 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['nan'])
elif second_to_last_state == 2 and last_state == 0 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['nas'])
elif second_to_last_state == 2 and last_state == 1 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['nha'])
elif second_to_last_state == 2 and last_state == 1 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['nhh'])
elif second_to_last_state == 2 and last_state == 1 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['nhn'])
elif second_to_last_state == 2 and last_state == 1 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['nhs'])
elif second_to_last_state == 2 and last_state == 2 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['nna'])
elif second_to_last_state == 2 and last_state == 2 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['nnh'])
elif second_to_last_state == 2 and last_state == 2 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['nnn'])
elif second_to_last_state == 2 and last_state == 2 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['nns'])
elif second_to_last_state == 2 and last_state == 3 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['nsa'])
elif second_to_last_state == 2 and last_state == 3 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['nsh'])
elif second_to_last_state == 2 and last_state == 3 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['nsn'])
elif second_to_last_state == 2 and last_state == 3 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['nss'])
###################################################################
elif second_to_last_state == 3 and last_state == 0 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['saa'])
elif second_to_last_state == 3 and last_state == 0 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['sah'])
elif second_to_last_state == 3 and last_state == 0 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['san'])
elif second_to_last_state == 3 and last_state == 0 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['sas'])
elif second_to_last_state == 3 and last_state == 1 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['sha'])
elif second_to_last_state == 3 and last_state == 1 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['shh'])
elif second_to_last_state == 3 and last_state == 1 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['shn'])
elif second_to_last_state == 3 and last_state == 1 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['shs'])
elif second_to_last_state == 3 and last_state == 2 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['sna'])
elif second_to_last_state == 3 and last_state == 2 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['snh'])
elif second_to_last_state == 3 and last_state == 2 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['snn'])
elif second_to_last_state == 3 and last_state == 2 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['sns'])
elif second_to_last_state == 3 and last_state == 3 and state == 0:
loss -= np.log(self.emo_trans_prob_dict['ssa'])
elif second_to_last_state == 3 and last_state == 3 and state == 1:
loss -= np.log(self.emo_trans_prob_dict['ssh'])
elif second_to_last_state == 3 and last_state == 3 and state == 2:
loss -= np.log(self.emo_trans_prob_dict['ssn'])
elif second_to_last_state == 3 and last_state == 3 and state == 3:
loss -= np.log(self.emo_trans_prob_dict['sss'])
'''
new_beam_state = beam_state.update(speaker, state, loss)
return new_beam_state