# Discriminate natural versus loudspeaker emitted speech

## TL;DR

This paper presents a method to distinguish between natural human speech and speech played back from loudspeakers, using deep neural networks and audio features, to improve voice interface security and reliability.

## Contribution

It introduces a new dataset and demonstrates that combining SoundNet and VGGish embeddings achieves up to 90% accuracy in classifying speech origin.

## Key findings

- Deep neural networks can effectively discriminate speech sources.
- Combining SoundNet and VGGish features improves accuracy.
- Achieved approximately 90% classification accuracy.

## Abstract

In this work, we address a novel, but potentially emerging, problem of discriminating the natural human voices and those played back by any kind of audio devices in the context of interactions with in-house voice user interface. The tackled problem may find relevant applications in (1) the far-field voice interactions of vocal interfaces such as Amazon Echo, Google Home, Facebook Portal, etc, and (2) the replay spoofing attack detection. The detection of loudspeaker emitted speech will help avoid false wake-ups or unintended interactions with the devices in the first application, while eliminating attacks involve the replay of recordings collected from enrolled speakers in the second one. At first we collect a real-world dataset under well-controlled conditions containing two classes: recorded speeches directly spoken by numerous people (considered as the natural speech), and recorded speeches played back from various loudspeakers (considered as the loudspeaker emitted speech). Then from this dataset, we build prediction models based on Deep Neural Network (DNN) for which different combination of audio features have been considered. Experiment results confirm the feasibility of the task where the combination of audio embeddings extracted from SoundNet and VGGish network yields the classification accuracy up to about 90%.