Papers › Speech Emotion Recognition Using Speech Feature and Word Embedding
Speech Emotion Recognition Using Speech Feature and Word Embedding
Bagus Tris Atmaja, Kiyoaki Shirai, and Masato Akagi
—Emotion recognition can be performed automatically from many modalities. This paper presents a categorical speech emotion recognition using speech features and word embedding. Text features can be combined with speech features to improve emotion recognition accuracy, and both features can be obtained from speech. Here, we use speech segments, by removing silences in an utterance, where the acoustic feature is extracted for speech-based emotion recognition. Word embedding is used as an input feature for text emotion recognition and a combination of both features is proposed for performance improvement purposes. Two unidirectional LSTM layers are used for text and fully connected layers are applied for acoustic emotion recognition. Both networks then are merged by fully connected networks in an early fusion way to produce one of four predicted emotion categories. The result shows the combination of speech and text achieve higher accuracy i.e. 75.49% compared to speech only with 58.29% or text only emotion recognition with 68.01%. This result also outperforms the previously proposed methods by others using the same dataset on the same modalities
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Speech Emotion Recognition | IEMOCAP | LSTM+FC | F1 | - | #8 of 8 | Archive leaderboard | report |
| Speech Emotion Recognition | IEMOCAP | LSTM+FC | UA | - | #8 of 8 | Archive leaderboard | report |
| Speech Emotion Recognition | IEMOCAP | LSTM+FC | WA | 0.755 | #8 of 8 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections