Use of affect based interaction classification for continuous emotion tracking

Khaki, Hossein; Erzin, Engin

doi:10.1109/icassp.2017.7952683

Cited by 2 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Stuhlsatz et al [32] employed Restricted Boltzmann Machines (RBM) to extract discriminative features from the raw signal and developed a Generalized Discriminant Analysis (GerDA). Sainath et al [4,33] proposed a convolutional long short-term memory deep neural network (CLDNN) model able to reduce temporal and frequency variations in speech emotion recognition.…”

Section: Speech Emotion Recognition Modelsmentioning

confidence: 99%

A Novel Heterogeneous Parallel Convolution Bi-LSTM for Speech Emotion Recognition

2021

View full text Add to dashboard Cite

Speech emotion recognition is a substantial component of natural language processing (NLP). It has strict requirements for the effectiveness of feature extraction and that of the acoustic model. With that in mind, a Heterogeneous Parallel Convolution Bi-LSTM model is proposed to address the challenges. It consists of two heterogeneous branches: the left one contains two dense layers and a Bi-LSTM layer, while the right one contains a dense layer, a convolution layer, and a Bi-LSTM layer. It can exploit the spatiotemporal information more effectively, and achieves 84.65%, 79.67%, and 56.50% unweighted average recalls on the benchmark databases EMODB, CASIA, and SAVEE, respectively. Compared with the previous research results, the proposed model achieves better performance stably.

show abstract

Section: Speech Emotion Recognition Modelsmentioning

confidence: 99%

A Novel Heterogeneous Parallel Convolution Bi-LSTM for Speech Emotion Recognition

2021

View full text Add to dashboard Cite

show abstract

“…In the past decades, SER has attracted widespread concern of researchers, and many tremendous achievements have been made. For example, SER finds its applications in Human-Computer Interaction (HCI), robotics, mobile computing, and computer games [2][3][4][5][6]. With the fast development of Artificial Intelligence (AI), HCI becomes increasingly convenient and friendly by adding emotions to machines.…”

Section: Introductionmentioning

confidence: 99%

A Novel Heterogeneous Parallel Convolution Bi-LSTM for Speech Emotion Recognition

Zhang¹,

Huang²,

Han³

2021

Preprint

View full text Add to dashboard Cite

Speech emotion recognition remains a heavy lifting in natural language processing. It has strict requirements to the effectiveness of feature extraction and that of acoustic model. With that in mind, a Heterogeneous Parallel Convolution Bi-LSTM model is proposed to address these challenges. It consists of two heterogeneous branches: the left one contains two dense layers and a Bi-LSTM layer, while the right one contains a dense layer, a convolution layer, and a Bi-LSTM layer. It can exploit the spatiotemporal information more effectively, and achieves 84.65%, 79.67%, and 56.50% unweighted average recall on the benchmark databases EMODB, CASIA, and SAVEE, respectively. Compared with the previous research results, the proposed model achieves better performance stably.

show abstract

Use of affect based interaction classification for continuous emotion tracking

Cited by 2 publications

References 19 publications

A Novel Heterogeneous Parallel Convolution Bi-LSTM for Speech Emotion Recognition

A Novel Heterogeneous Parallel Convolution Bi-LSTM for Speech Emotion Recognition

A Novel Heterogeneous Parallel Convolution Bi-LSTM for Speech Emotion Recognition

Contact Info

Product

Resources

About