Continuous Authentication for Voice Assistants

Voice has become an increasingly popular User Interaction (UI) channel, mainly contributing to the ongoing trend of wearables, smart vehicles, and home automation systems. Voice assistants such as Siri, Google Now and Cortana, have become our everyday fixtures, especially in scenarios where touch interfaces are inconvenient or even dangerous to use, such as driving or exercising. Nevertheless, the open nature of the voice channel makes voice assistants difficult to secure and exposed to various attacks as demonstrated by security researchers. In this paper, we present VAuth, the first system that provides continuous and usable authentication for voice assistants. We design VAuth to fit in various widely-adopted wearable devices, such as eyeglasses, earphones/buds and necklaces, where it collects the body-surface vibrations of the user and matches it with the speech signal received by the voice assistant's microphone. VAuth guarantees that the voice assistant executes only the commands that originate from the voice of the owner. We have evaluated VAuth with 18 users and 30 voice commands and find it to achieve an almost perfect matching accuracy with less than 0.1% false positive rate, regardless of VAuth's position on the body and the user's language, accent or mobility. VAuth successfully thwarts different practical attacks, such as replayed attacks, mangled voice attacks, or impersonation attacks. It also has low energy and latency overheads and is compatible with most existing voice assistants

A frequency-selective feedback model of auditory efferent suppression and its implications for the recognition of speech in noise

The potential contribution of the peripheral auditory efferent system to our understanding of speech in a background of competing noise was studied using a computer model of the auditory periphery and assessed using an automatic speech recognition system. A previous study had shown that a fixed efferent attenuation applied to all channels of a multi-channel model could improve the recognition of connected digit triplets in noise [G. J. Brown, R. T. Ferry, and R. Meddis, J. Acoust. Soc. Am. 127, 943?954 (2010)]. In the current study an anatomically justified feedback loop was used to automatically regulate separate attenuation values for each auditory channel. This arrangement resulted in a further enhancement of speech recognition over fixed-attenuation conditions. Comparisons between multi-talker babble and pink noise interference conditions suggest that the benefit originates from the model?s ability to modify the amount of suppression in each channel separately according to the spectral shape of the interfering sounds

Speech Decomposition and Enhancement

The goal of this study is to investigate the roles of steady-state speech sounds and transitions between these sounds in the intelligibility of speech. The motivation for this approach is that the auditory system may be particularly sensitive to time-varying frequency edges, which in speech are produced primarily by transitions between vowels and consonants and within vowels. The possibility that selectively amplifying these edges may enhance speech intelligibility is examined. Computer algorithms to decompose speech into two different components were developed. One component, which is defined as a tonal component, was intended to predominately include formant activity. The second component, which is defined as a non-tonal component, was intended to predominately include transitions between and within formants.The approach to the decomposition is to use a set of time-varying filters whose center frequencies and bandwidths are controlled to identify the strongest formant components in speech. Each center frequency and bandwidth is estimated based on FM and AM information of each formant component. The tonal component is composed of the sum of the filter outputs. The non-tonal component is defined as the difference between the original speech signal and the tonal component.The relative energy and intelligibility of the tonal and non-tonal components were compared to the original speech. Psychoacoustic growth functions were used to assess the intelligibility. Most of the speech energy was in the tonal component, but this component had a significantly lower maximum word recognition than the original and non-tonal component had. The non-tonal component averaged 2% of the original speech energy, but this component had almost equal maximum word recognition as the original speech. The non-tonal component was amplified and recombined with the original speech to generate enhanced speech. The energy of the enhanced speech was adjusted to be equal to the original speech, and the intelligibility of the enhanced speech was compared to the original speech in background noise. The enhanced speech showed higher recognition scores at lower SNRs, and the differences were significant. The original and enhanced speech showed similar recognition scores at higher SNRs. These results suggest that amplification of transient information can enhance the speech in noise and this enhancement method is more effective at severe noise conditions