Efficient Invariant Features for Sensor Variability Compensation in Speaker Recognition

In this paper, we investigate the use of invariant features for speaker recognition. Owing to their characteristics, these features are introduced to cope with the difficult and challenging problem of sensor variability and the source of performance degradation inherent in speaker recognition systems. Our experiments show: (1) the effectiveness of these features in match cases; (2) the benefit of combining these features with the mel frequency cepstral coefficients to exploit their discrimination power under uncontrolled conditions (mismatch cases). Consequently, the proposed invariant features result in a performance improvement as demonstrated by a reduction in the equal error rate and the minimum decision cost function compared to the GMM-UBM speaker recognition systems based on MFCC features

Multimodal person recognition for human-vehicle interaction

Next-generation vehicles will undoubtedly feature biometric person recognition as part of an effort to improve the driving experience. Today's technology prevents such systems from operating satisfactorily under adverse conditions. A proposed framework for achieving person recognition successfully combines different biometric modalities, borne out in two case studies

Detecting User Engagement in Everyday Conversations

This paper presents a novel application of speech emotion recognition: estimation of the level of conversational engagement between users of a voice communication system. We begin by using machine learning techniques, such as the support vector machine (SVM), to classify users' emotions as expressed in individual utterances. However, this alone fails to model the temporal and interactive aspects of conversational engagement. We therefore propose the use of a multilevel structure based on coupled hidden Markov models (HMM) to estimate engagement levels in continuous natural speech. The first level is comprised of SVM-based classifiers that recognize emotional states, which could be (e.g.) discrete emotion types or arousal/valence levels. A high-level HMM then uses these emotional states as input, estimating users' engagement in conversation by decoding the internal states of the HMM. We report experimental results obtained by applying our algorithms to the LDC Emotional Prosody and CallFriend speech corpora.Comment: 4 pages (A4), 1 figure (EPS

A Review of Verbal and Non-Verbal Human-Robot Interactive Communication

In this paper, an overview of human-robot interactive communication is presented, covering verbal as well as non-verbal aspects of human-robot interaction. Following a historical introduction, and motivation towards fluid human-robot communication, ten desiderata are proposed, which provide an organizational axis both of recent as well as of future research on human-robot communication. Then, the ten desiderata are examined in detail, culminating to a unifying discussion, and a forward-looking conclusion

Harnessing AI for Speech Reconstruction using Multi-view Silent Video Feed

Speechreading or lipreading is the technique of understanding and getting phonetic features from a speaker's visual features such as movement of lips, face, teeth and tongue. It has a wide range of multimedia applications such as in surveillance, Internet telephony, and as an aid to a person with hearing impairments. However, most of the work in speechreading has been limited to text generation from silent videos. Recently, research has started venturing into generating (audio) speech from silent video sequences but there have been no developments thus far in dealing with divergent views and poses of a speaker. Thus although, we have multiple camera feeds for the speech of a user, but we have failed in using these multiple video feeds for dealing with the different poses. To this end, this paper presents the world's first ever multi-view speech reading and reconstruction system. This work encompasses the boundaries of multimedia research by putting forth a model which leverages silent video feeds from multiple cameras recording the same subject to generate intelligent speech for a speaker. Initial results confirm the usefulness of exploiting multiple camera views in building an efficient speech reading and reconstruction system. It further shows the optimal placement of cameras which would lead to the maximum intelligibility of speech. Next, it lays out various innovative applications for the proposed system focusing on its potential prodigious impact in not just security arena but in many other multimedia analytics problems.Comment: 2018 ACM Multimedia Conference (MM '18), October 22--26, 2018, Seoul, Republic of Kore

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