speechbrain.utils.EDER module

Calculates Emotion Diarization Error Rate (EDER) which is the sum of Missed Emotion (ME), False Alarm (FA), and Confusion (CF).

Authors

• Yingzhi Wang 2023

Summary

Functions:

EDER	Calculates the EDER value
distribute_overlap	Distributes the overlapped speech equally among the adjacent segments with different emotions.
getOverlap	get the overlapped length of two intervals
is_overlapped	Returns True if segments are overlapping.
merge_ssegs_same_emotion_adjacent	Merge adjacent sub-segs if they are the same emotion.
reference_to_lol	change reference to a list of list :param id (str): :type id (str): id of the utterance :param duration (float): :type duration (float): duration of the utterance :param emotion (list of dicts): e.g.

Reference

speechbrain.utils.EDER.EDER(prediction, id, duration, emotion, window_length, stride)

Calculates the EDER value

Args:

prediction (list): a list of frame-wise predictions of the utterance id (str): id of the utterance duration (float): duration of the utterance emotion (list of dicts): the ground truth emotion and its duration,

e.g. [{‘emo’: ‘angry’, ‘start’: 1.016, ‘end’: 6.336}]

window_length (float): the frame length used for frame-wise prediction stride (float): the frame length used for frame-wise prediction

Returns:

float: the calculted EDER for the utterance

Example

>>> from speechbrain.utils.EDER import EDER
>>> prediction=['n', 'n', 'n', 'a', 'a', 'a']
>>> id="spk1_1"
>>> duration=1.22
>>> emotion=[{'emo': 'angry', 'start': 0.39, 'end': 1.10}]
>>> window_length = 0.2
>>> stride = 0.2
>>> EDER(prediction, id, duration, emotion, window_length, stride)
0.2704918032786885

speechbrain.utils.EDER.getOverlap(a, b)

get the overlapped length of two intervals

Parameters:

• a (list) –

• b (list) –

• Returns – float: overlapped length

Example

>>> from speechbrain.utils.EDER import getOverlap
>>> interval1=[1.2, 3.4]
>>> interval2=[2.3, 4.5]
>>> getOverlap(interval1, interval2)
1.1

speechbrain.utils.EDER.is_overlapped(end1, start2)

Returns True if segments are overlapping.

Parameters:

• end1 (float) – End time of the first segment.

• start2 (float) – Start time of the second segment.

Returns:

overlapped – True of segments overlapped else False.

Return type:

bool

Example

>>> from speechbrain.processing import diarization as diar
>>> diar.is_overlapped(5.5, 3.4)
True
>>> diar.is_overlapped(5.5, 6.4)
False

speechbrain.utils.EDER.merge_ssegs_same_emotion_adjacent(lol)

Merge adjacent sub-segs if they are the same emotion. :param lol: Each list contains [utt_id, sseg_start, sseg_end, emo_label]. :type lol: list of list

Returns:

new_lol – new_lol contains adjacent segments merged from the same emotion ID.

Return type:

list of list

Example

>>> from speechbrain.utils.EDER import merge_ssegs_same_emotion_adjacent
>>> lol=[['u1', 0.0, 7.0, 'a'],
... ['u1', 7.0, 9.0, 'a'],
... ['u1', 9.0, 11.0, 'n'],
... ['u1', 11.0, 13.0, 'n'],
... ['u1', 13.0, 15.0, 'n'],
... ['u1', 15.0, 16.0, 'a']]
>>> merge_ssegs_same_emotion_adjacent(lol)
[['u1', 0.0, 9.0, 'a'], ['u1', 9.0, 15.0, 'n'], ['u1', 15.0, 16.0, 'a']]

speechbrain.utils.EDER.reference_to_lol(id, duration, emotion)

change reference to a list of list :param id (str): :type id (str): id of the utterance :param duration (float): :type duration (float): duration of the utterance :param emotion (list of dicts): e.g. [{‘emo’: ‘angry’, ‘start’: 1.016, ‘end’: 6.336}] :type emotion (list of dicts): the ground truth emotion and its duration,

Returns:

lol – It has each list structure as [rec_id, sseg_start, sseg_end, spkr_id].

Return type:

list of list

Example

>>> from speechbrain.utils.EDER import reference_to_lol
>>> id="u1"
>>> duration=8.0
>>> emotion=[{'emo': 'angry', 'start': 1.016, 'end': 6.336}]
>>> reference_to_lol(id, duration, emotion)
[['u1', 0, 1.016, 'n'], ['u1', 1.016, 6.336, 'a'], ['u1', 6.336, 8.0, 'n']]

speechbrain.utils.EDER.distribute_overlap(lol)

Distributes the overlapped speech equally among the adjacent segments with different emotions.

Parameters:

lol (list of list) – It has each list structure as [rec_id, sseg_start, sseg_end, spkr_id].

Returns:

new_lol – It contains the overlapped part equally divided among the adjacent segments with different emotion IDs.

Return type:

list of list

Example

>>> from speechbrain.processing import diarization as diar
>>> lol = [['r1', 5.5, 9.0, 's1'],
... ['r1', 8.0, 11.0, 's2'],
... ['r1', 11.5, 13.0, 's2'],
... ['r1', 12.0, 15.0, 's1']]
>>> diar.distribute_overlap(lol)
[['r1', 5.5, 8.5, 's1'], ['r1', 8.5, 11.0, 's2'], ['r1', 11.5, 12.5, 's2'], ['r1', 12.5, 15.0, 's1']]