DNN-based Emotion Recognition Based on Bottleneck Acoustic Features and Lexical Features

Eesung Kim · Jong Won Shin

54 citations0/0 files storedIEEE International Conference on Acoustics Speech and Signal Processing

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Emotion and Mood Recognition Speech Recognition and Synthesis Sentiment Analysis and Opinion Mining

Abstract

In this paper, we propose a novel emotion recognition method to reflect affect salient information using acoustic and lexical features. The acoustic features are extracted from the speech signal by applying statistical functionals of emotionally high-level features derived from Deep Neural Network (DNN). These acoustic features are early fused with two types of lexical features extracted from the text transcription of the speech signal, which are the distributed representation and affective lexicon-based dimensions. The fused features are fed to another DNN for utterance-level emotion classification. Experimental results on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) multimodal dataset showed 75.5% in unweighted accuracy recall, which outperformed the best results reported previously in the multimodal emotion recognition using acoustic and lexical features.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 54 citations exist upstream.

References

2008IEMOCAP: interactive emotional dyadic motion capture database 2010Survey on speech emotion recognition: Features, classification schemes, and databases 2015The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing 2011Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge 2013The INTERSPEECH 2013 computational paralinguistics challenge: social signals, conflict, emotion, autism 2011Deep neural networks for acoustic emotion recognition: Raising the benchmarks 2015Speech emotion recognition with acoustic and lexical features 2018Speech Emotion Recognition Using Spectrogram & Phoneme Embedding

Cited by

2023TDFNet: Transformer-Based Deep-Scale Fusion Network for Multimodal Emotion Recognition 2022Speech Emotion Recognition Based on Self-Attention Weight Correction for Acoustic and Text Features

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.