High-level feature representation using recurrent neural network for speech emotion recognition

By Jinkyu Lee, Ivan J. Tashev. Interspeech, 2015

385 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/9bfd45ce4d6554ceb570a712ece8e0d5d9139872

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Experimental and Cognitive Psychology

In Signal Processing

Citations in the corpus

A Comprehensive Review of Speech Emotion Recognition Systems (2021, methodology): “An efﬁcient approach based on RNN for emotion recognition was presented in [68].”
Automatic speech emotion recognition using recurrent neural networks with local attention (2017, methodology): “However, works in [7] and [8] have both applied ELM for the utterance level aggregation, despite the fact that they already adopted a CTC-style recurrent network underneath [8].”
Improving speech emotion recognition via Transformer-based Predictive Coding through transfer learning (2018, background): “Speech emotion recognition, as an important aspect of emotion recognition, has shown tremendous progress under the development of deep learning [1-10].”
3-D Convolutional Recurrent Neural Networks With Attention Model for Speech Emotion Recognition (2018, background): “[5] applied a recurrent neural network (RNN) to learn long-range temporal relationships for SER.”
Tensor Fusion Network for Multimodal Sentiment Analysis (2017, methodology): “and emotion recognition (retrained for sentiment analysis): HL-RNN (Lee and Tashev, 2015) uses an LSTM on high-level audio features.”
Recent Advances in Recurrent Neural Networks (2017, background): “In [107], a deep BLSTM is introduced for speech emotion recognition.”
Speech Emotion Recognition Using Deep Learning Techniques: A Review (2019, background): “information, where the outputs and inputs are interdependent [65].”
Multimodal Emotion Recognition using Deep Learning (2021, methodology): “[54] shows that using RNN bidirectional- (Bi-LSTM) is better for extracting essential speech characteristics for better speech”
Predicting Expressive Speaking Style from Text in End-To-End Speech Synthesis (2018, background): “Similarly, substantial effort has gone into modeling emotion, but has traditionally required keywords, semantic representations, or labels for model training; recent examples include [11], [12], [13], [14], and [15], [16]).”
Speech emotion recognition using convolutional and Recurrent Neural Networks (2016, methodology): “The SER system using RNNs was proposed in [14], which accounts for long contextual effect in emotional speech and the uncertainty of emotional labels.”
A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, methodology): “To decrease the training time and complexity based on LSTM and attention mechanisms to predict the sentiment polarity, Xue et al. [151] designed a Gated Convolutional network with Aspect Embedding (GCAE) for ACSA and ATSA, which are more efficient and simpler than the RNN-based model.”
Multimodal Speech Emotion Recognition Using Audio and Text (2018, neutral): “)-ELM [34] 0.”