Prepoznavanje emocija iz govora temeljem akustičkih značajki primjenom metodologije neuronskih mreža

master's thesis

• Title (english): Speech emotion recognition based on acoustic features using a neural network methodology
• Author: Ivan Fanjek
• Mentor: Tomislav Stipančić (mentor)
• Granter: University of Zagreb; Faculty of Mechanical Engineering and Naval Architecture; Zagreb
• Defense date and country: 2021-01-21, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Mechanical Engineering

Abstract

Govor je primarni način komuniciranja između ljudi, a izražene emocije igraju veliku ulogu u razumijevanju konteksta poruke prenesene govorom. Kako se razvija komunikacija između ljudi i računala potrebno je omogućiti računalima prepoznavanje ljudskih emocija. U ovome radu izrađen je softver koji na temelju umjetnih neuronskih mreža iz govora prepoznaje izražene emocije. Umjetne neuronske mreže obrađuju akustičke značajke iz govora kako bi prepoznale emocije. Izrađena su dva različita modela umjetnih neuronskih mreža, te su oba modela trenirana na dostupnim bazama podataka. Dva izrađena modela su eksperimentalno evaluirana na ljudskim subjektima.

Abstract (english)

Speech is the primary means of communication between people, and expressed emotions play an important role in understanding the context of the message sent through it. As the communication between humans and computers develops it is necessary to enable machines to recognize human emotions. In this paper, a software has been developed which is based on artificial neural networks to recognize expressed emotions from speech. Artificial neural networks process acoustic features of speech to recognize emotions. Two different models of artificial neural networks have been developed, and both models have been trained on available databases. The two developed models were experimentally evaluated by human subjects.

• Keywords: emocije; akustičke značajke; umjetne neuronske mreže; konvolucijske neuronske mreže; duga kratkoročna memorija
• Keywords (english): emotions; acoustic features; artificial neural networks; convolutional neural networks; long short-term memory
• Language: croatian
• URN:NBN: urn:nbn:hr:235:200619
• Study programme: Title: Mechanical Engineering; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra inženjer/inženjerka strojarstva (magistar/magistra inženjer/inženjerka strojarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Open access
• Created on: 2021-01-18 15:25:44