# Analyzing Input and Output Representations for Speech-Driven Gesture Generation

## TL;DR

This paper introduces a deep learning framework that uses representation learning to generate human-like gestures from speech, improving gesture quality and relevance in human-agent interactions.

## Contribution

It proposes a novel two-step approach combining autoencoder-based motion representation learning with speech-to-motion encoding, enhancing gesture generation accuracy.

## Key findings

- MFCCs and prosodic features improve gesture relevance
- Representation learning enhances gesture naturalness
- User study confirms effectiveness of the approach

## Abstract

This paper presents a novel framework for automatic speech-driven gesture generation, applicable to human-agent interaction including both virtual agents and robots. Specifically, we extend recent deep-learning-based, data-driven methods for speech-driven gesture generation by incorporating representation learning. Our model takes speech as input and produces gestures as output, in the form of a sequence of 3D coordinates. Our approach consists of two steps. First, we learn a lower-dimensional representation of human motion using a denoising autoencoder neural network, consisting of a motion encoder MotionE and a motion decoder MotionD. The learned representation preserves the most important aspects of the human pose variation while removing less relevant variation. Second, we train a novel encoder network SpeechE to map from speech to a corresponding motion representation with reduced dimensionality. At test time, the speech encoder and the motion decoder networks are combined: SpeechE predicts motion representations based on a given speech signal and MotionD then decodes these representations to produce motion sequences. We evaluate different representation sizes in order to find the most effective dimensionality for the representation. We also evaluate the effects of using different speech features as input to the model. We find that mel-frequency cepstral coefficients (MFCCs), alone or combined with prosodic features, perform the best. The results of a subsequent user study confirm the benefits of the representation learning.