Synthesizing naturalistic laughter: An exploratory study on modeling voiced laughter with speech synthesis techniques

Date of Publication

2013

Document Type

Bachelor's Thesis

Degree Name

Bachelor of Science in Computer Science

Subject Categories

Computer Sciences

College

College of Computer Studies

Department/Unit

Computer Science

Thesis Adviser

Jocelynn Cu

Defense Panel Chair

Ethel Ong

Defense Panel Member

Merlin Teodosia C. Suarez
Adrianne John Galang

Abstract/Summary

This study focuses on the synthesis of naturalistic voiced laughter, and attempts to address the wide gap present in applications that involve synthetic agents. This gap lies in the interactions between human and these agents, which can in part be filled through the emulation and expression of paralinguistic sounds such as laughter. Most agents speak through a synthesized voice, but inserting a prerecord laughter sound in between sentences proved to score low in participation tests (Trouvain & Schroder, 2004), thus making for a compelling reason to pursue computer-generated laughter. This involves the analysis of a set of acoustic features including, but not limited to, pitch and MFCCs present in voiced laughter, and consequently the synthesis of laughter using concatenative diphone synthesis, articulatory synthesis and hidden Markov model-based statistical parametric synthesis techniques.

With this in mind, the goal is to generate laughter that is perceived as acceptable and natural by evaluators. This can be validated through subjective evaluation tests where an evaluator determines the synthesized laughter from a set of clips. The results of this work show that while evaluators are primarily able to identify natural laughter from synthesized laughter, there is much doubt and little agreement on whether or not these clips were even truly natural or not. Aside from articulatory synthesis-which was consistently rated lowly- the concatenative diphone synthesis and statistical parametric synthesis techniques proved quite effective in synthesizing laughter that was rated to be even more naturalistic than samples from a spontaneous laughter database. Differences between male and female evaluator groups were found and identified through the use of decision tree models and are used to identify how certain features may have influenced the evaluation score.

Abstract Format

html

Language

English

Format

Print

Accession Number

TU18486

Shelf Location

Archives, The Learning Commons, 12F, Henry Sy Sr. Hall

Physical Description

1v. various foliations : illustrations (some colored) ; 28 cm.

Keywords

Speech processing systems; Speech synthesis; Laughter

Embargo Period

2-4-2022

This document is currently not available here.

Share

COinS