Automatic Speech Emotion Recognition Using Recurrent Neural Networks with Local Attention
Automatic emotion recognition from speech is a challenging task which significantly relies on the emotional relevance of specific features extracted from the speech signal. In this study, our goal is to use deep learning to automatically discover emotionally relevant features. It is shown that using a deep Recurrent Neural Network (RNN), we can learn both the short-time frame-level acoustic features that are emotionally relevant, as well as an appropriate temporal aggregation of those features into a compact sentence-level representation. Moreover, we propose a novel strategy for feature pooling over time using attention mechanism with the RNN, which is able to focus on local regions of a speech signal that are more emotionally salient. The proposed solution was tested on the IEMOCAP emotion corpus, and was shown to provide more accurate predictions compared to existing emotion recognition algorithms.
- Series:
- Microsoft Research Talks
- Date:
- Speakers:
- Matt Mirsamadi
- Affiliation:
- The University of Texas at Dallas
-
-
Cha Zhang
Principal Researcher
-
-
Series: Microsoft Research Talks
-
-
-
-
Galea: The Bridge Between Mixed Reality and Neurotechnology
Speakers:- Eva Esteban,
- Conor Russomanno
-
Current and Future Application of BCIs
Speakers:- Christoph Guger
-
Challenges in Evolving a Successful Database Product (SQL Server) to a Cloud Service (SQL Azure)
Speakers:- Hanuma Kodavalla,
- Phil Bernstein
-
Improving text prediction accuracy using neurophysiology
Speakers:- Sophia Mehdizadeh
-
-
DIABLo: a Deep Individual-Agnostic Binaural Localizer
Speakers:- Shoken Kaneko
-
-
Recent Efforts Towards Efficient And Scalable Neural Waveform Coding
Speakers:- Kai Zhen
-
-
Audio-based Toxic Language Detection
Speakers:- Midia Yousefi
-
-
From SqueezeNet to SqueezeBERT: Developing Efficient Deep Neural Networks
Speakers:- Sujeeth Bharadwaj
-
Hope Speech and Help Speech: Surfacing Positivity Amidst Hate
Speakers:- Monojit Choudhury
-
-
-
-
-
'F' to 'A' on the N.Y. Regents Science Exams: An Overview of the Aristo Project
Speakers:- Peter Clark
-
Checkpointing the Un-checkpointable: the Split-Process Approach for MPI and Formal Verification
Speakers:- Gene Cooperman
-
Learning Structured Models for Safe Robot Control
Speakers:- Ashish Kapoor
-
-