View Proposal

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Proposer

Matthew Aylett

Title

Robot Voice Separation using LSTMs

Goal

Given and audio signal with a mixed 'robot' voice and dialog partner cancel out the robot voice.

Description

Current robots (furhat, haru etc) typically stop listening to their microphone when speaking to avoid interfering with speech recognition results. This means robots can't be interrupted and can't produce back channels (the yeah/okay that shows you are listening). In this project you will extend a small audio corpus built to test robot voice separation, set up the Kaldi ASR system, design and train an neural net solution for altering ASR parameters to remove the effect of the robot voice and maintain recognition accuracy.

Resources

Kaldi, baseline corpora

Background

Aylett, M. P., & Romeo, M. (2023, July). You Don’t Need to Speak, You Need to Listen: Robot Interaction and Human-Like Turn-Taking. In Proceedings of the 5th International Conference on Conversational User Interfaces (pp. 1-5).

Url

Difficulty Level

High

Ethical Approval

None

Number Of Students

2

Supervisor

Matthew Aylett

Keywords

social robotics, speech technology, dialog systems, conversational user interfaces

Degrees

Bachelor of Science in Computer Science
Master of Science in Artificial Intelligence
Master of Science in Data Science
Master of Science in Human Robot Interaction
Master of Science in Robotics