Speech Emotion Recognition via a Representation Learning (Completed)

Description

Investigating algorithms (or approaches) to improve the performance of speech emotion recognition system that classifies speech signal into sets of emotion label such as "happy", "sad", "angry", and "neutral". The internship includes research, implementation, and experiment for target tasks above.

Candidate Qualifications

• Strong attitude for the investigation
• Understanding of recent algorithm in deep learning literature (i.e. BERT from google)
• Experience of implementing an algorithm using Tensorflow (optional)
• Familiar with speech signal processing (optional)

Expected Internship Period

• Minimum four months - excluding period for the preliminary study (Tensorflow, base ML algorithms)

Contact

• Seunghyun Yoon (mysmilesh@snu.ac.kr) (Completed)

• click to apply (Completed)

End-to-end text-to-speech (Completed)

Description

Investigating end-to-end learning approaches to building a text-to-speech system that converts natural language text into human speech. The internship includes research, implementation, and experiment for target tasks above.

Candidate Qualifications

• Strong attitude for the investigation
• Understanding of recent algorithm in deep learning literature (i.e. Tacotron from google)
• Experience of implementing an algorithm using Tensorflow or PyTorch (optional)

Expected Internship Period

• Minimum four months - excluding period for the preliminary study (Tensorflow, base ML algorithms)

Contact

• Joongbo Shin (jbshin@snu.ac.kr) (Completed)