# Personalized Task Load Prediction in Speech Communication

## TL;DR

This paper introduces a personalized framework for predicting cognitive load in speech communication, accounting for individual differences and environmental factors to improve quality assessment accuracy.

## Contribution

It presents a novel evaluation framework and machine learning model that incorporate listener personality and preferences for personalized task load prediction.

## Key findings

- Significant relationships between stimulus quality and listener's valence and personality.
- Improved correlation coefficient from 0.48 to 0.76 when considering individual differences.
- Framework enables personalized audio quality assessment beyond traditional models.

## Abstract

Estimating the quality of remote speech communication is a complex task influenced by the speaker, transmission channel, and listener. For example, the degradation of transmission quality can increase listeners' cognitive load, which can influence the overall perceived quality of the conversation. This paper presents a framework that isolates quality-dependent changes and controls most outside influencing factors like personal preference in a simulated conversational environment. The performed statistical analysis finds significant relationships between stimulus quality and the listener's valence and personality (agreeableness and openness) and, similarly, between the perceived task load during the listening task and the listener's personality and frustration intolerance. The machine learning model of the task load prediction improves the correlation coefficients from 0.48 to 0.76 when listeners' individuality is considered. The proposed evaluation framework and results pave the way for personalized audio quality assessment that includes speakers' and listeners' individuality beyond conventional channel modeling.