SEGAA: A Unified Approach to Predicting Age, Gender, and Emotion in Speech

Author:

Aron R, Indra Sigicharla, Chirag Periwal, Mohanaprasad K, Nithya Darisini P S, Sourabh Tiwari, Shivani Arora

Keyword:

Electrical Engineering and Systems Science, Audio and Speech Processing, Audio and Speech Processing (eess.AS), Artificial Intelligence (cs.AI), Computation and Language (cs.CL), Machine Learning (cs.LG), Sound (cs.SD)

journal:

date:

2024-03-01 00:00:00

Abstract

The interpretation of human voices holds importance across various applications. This study ventures into predicting age, gender, and emotion from vocal cues, a field with vast applications. Voice analysis tech advancements span domains, from improving customer interactions to enhancing healthcare and retail experiences. Discerning emotions aids mental health, while age and gender detection are vital in various contexts. Exploring deep learning models for these predictions involves comparing single, multi-output, and sequential models highlighted in this paper. Sourcing suitable data posed challenges, resulting in the amalgamation of the CREMA-D and EMO-DB datasets. Prior work showed promise in individual predictions, but limited research considered all three variables simultaneously. This paper identifies flaws in an individual model approach and advocates for our novel multi-output learning architecture Speech-based Emotion Gender and Age Analysis (SEGAA) model. The experiments suggest that Multi-output models perform comparably to individual models, efficiently capturing the intricate relationships between variables and speech inputs, all while achieving improved runtime.

PDF: SEGAA: A Unified Approach to Predicting Age, Gender, and Emotion in Speech.pdf