Categories: FAANG

Speech Emotion: Investigating Model Representations, Multi-Task Learning and Knowledge Distillation

Estimating dimensional emotions, such as activation, valence and dominance, from acoustic speech signals has been widely explored over the past few years. While accurate estimation of activation and dominance from speech seem to be possible, the same for valence remains challenging. Previous research has shown that the use of lexical information can improve valence estimation performance.
Lexical information can be obtained from pre-trained acoustic models, where the learned representations can improve valence estimation from speech. We investigate the use of pre-trained model representations…
AI Generated Robotic Content

Recent Posts

Pushing MiniMax H3 quality on an RTX 3070 8GB — movie screenshots, voice refs + 0.5MP workflow

Wanted to see how far I could push the quality using what I already have.…

5 hours ago

Agents, Graphs, Loops & More: A Look Inside How Game of Life Is Actually Architected

I’ve spent close to a decade watching this industry build conversational AI, first through Chatbots…

5 hours ago

AI-driven development lifecycle using Amazon Bedrock AgentCore

Engineering teams adopting the AI-Driven Development Lifecycle (AI-DLC) with Amazon Bedrock AgentCore and coding agents…

5 hours ago

Wikipedia Workers Unionize for the First Time

More than 200 people in roles such as engineering, finance, and communications will now be…

6 hours ago

Why organic chemistry may help build AI that can explain its answers

While most believe artificial intelligence (AI) is changing science, researchers at the University of Notre…

6 hours ago

REFACTOR-VLA: Unsupervised Library Learning of Typed Motor Programs

Most current vision-language-action (VLA) models—such as OpenVLA, π0, RT-2, and RDT-1B—are “monolithic.” This means they…

1 day ago