Discriminate Natural versus Loudspeaker Emitted Speech

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 avoiding 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%.Index Terms-Natural versus device emitted speech classification, voice interaction, audio features, deep neural network (DNN).

Sistema de identificación biométrico basado en reconocimiento de voz mediante coeficientes cepstrales para detección de spoofing en llamadas telefónicas

“…Esto generará un sistema de autentificación muy peculiar con alta aceptación para futuros trabajos. Le et al (2019), tienen como objetivo separar las voces naturales humanas de las voces reproducidas por cualquier tipo de dispositivo de audio en el contexto de una interacción en la interfaz de un usuario con su voz. La metodología utilizada implica recopilar información de diversos conjuntos de datos del mundo real para construir modelos predictivos basados en Deep Neural Network (DNN), los cuales se han desarrollado utilizando diferentes combinaciones de funciones de audio.…”