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. Author manuscript; available in PMC: 2020 May 1.
Published in final edited form as: Proc Conf Assoc Comput Linguist Meet. 2019 Jul;2019:6558–6569. doi: 10.18653/v1/p19-1656

Table 3:

Results for multimodal emotions analysis on IEMOCAP with aligned and non-aligned multimodal sequences.

Task Happy Sad Angry Neutral
Metric Acch F1h Acch F1h Acch F1h Acch F1h

(Word Aligned) IEMOCAP Emotions

EF-LSTM 86.0 84.2 80.2 80.5 85.2 84.5 67.8 67.1
LF-LSTM 85.1 86.3 78.9 81.7 84.7 83.0 67.1 67.6
RMFN (Liang et al., 2018) 87.5 85.8 83.8 82.9 85.1 84.6 69.5 69.1
MFM (Tsai etal.,2019) 90.2 85.8 88.4 86.1 87.5 86.7 72.1 68.1
RAVEN (Wang et al.,2019) 87.3 85.8 83.4 83.1 87.3 86.7 69.7 69.3
MCTN (Pham et al.,2019) 84.9 83.1 80.5 79.6 79.7 80.4 62.3 57.0

MulT (ours) 90.7 88.6 86.7 86.0 87.4 87.0 72.4 70.7

(Unaligned) IEMOCAP Emotions

CTC (Graves et al., 2006) + EF-LSTM 76.2 75.7 70.2 70.5 72.7 67.1 58.1 57.4
LF-LSTM 72.5 71.8 72.9 70.4 68.6 67.9 59.6 56.2
CTC + RAVEN (Wang et al., 2019) 77.0 76.8 67.6 65.6 65.0 64.1 62.0 59.5
CTC + MCTN (Pham et al., 2019) 80.5 77.5 72.0 71.7 64.9 65.6 49.4 49.3

MulT (ours) 84.8 81.9 77.7 74.1 73.9 70.2 62.5 59.l