Language Identification Using Visual Features

Automatic visual language identification (VLID) is the technology of using information derived from the visual appearance and movement of the speech articulators to iden- tify the language being spoken, without the use of any audio information. This technique for language identification (LID) is useful in situations in which conventional audio processing is ineffective (very noisy environments), or impossible (no audio signal is available). Research in this field is also beneficial in the related field of automatic lip-reading. This paper introduces several methods for visual language identification (VLID). They are based upon audio LID techniques, which exploit language phonology and phonotactics to discriminate languages. We show that VLID is possible in a speaker-dependent mode by discrimi- nating different languages spoken by an individual, and we then extend the technique to speaker-independent operation, taking pains to ensure that discrimination is not due to artefacts, either visual (e.g. skin-tone) or audio (e.g. rate of speaking). Although the low accuracy of visual speech recognition currently limits the performance of VLID, we can obtain an error-rate of < 10% in discriminating between Arabic and English on 19 speakers and using about 30s of visual speech

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

Language identification with suprasegmental cues: A study based on speech resynthesis

This paper proposes a new experimental paradigm to explore the discriminability of languages, a question which is crucial to the child born in a bilingual environment. This paradigm employs the speech resynthesis technique, enabling the experimenter to preserve or degrade acoustic cues such as phonotactics, syllabic rhythm or intonation from natural utterances. English and Japanese sentences were resynthesized, preserving broad phonotactics, rhythm and intonation (Condition 1), rhythm and intonation (Condition 2), intonation only (Condition 3), or rhythm only (Condition 4). The findings support the notion that syllabic rhythm is a necessary and sufficient cue for French adult subjects to discriminate English from Japanese sentences. The results are consistent with previous research using low-pass filtered speech, as well as with phonological theories predicting rhythmic differences between languages. Thus, the new methodology proposed appears to be well-suited to study language discrimination. Applications for other domains of psycholinguistic research and for automatic language identification are considered

Arabic Speaker-Independent Continuous Automatic Speech Recognition Based on a Phonetically Rich and Balanced Speech Corpus

This paper describes and proposes an efficient and effective framework for the design and development of a speaker-independent continuous automatic Arabic speech recognition system based on a phonetically rich and balanced speech corpus. The speech corpus contains a total of 415 sentences recorded by 40 (20 male and 20 female) Arabic native speakers from 11 different Arab countries representing the three major regions (Levant, Gulf, and Africa) in the Arab world. The proposed Arabic speech recognition system is based on the Carnegie Mellon University (CMU) Sphinx tools, and the Cambridge HTK tools were also used at some testing stages. The speech engine uses 3-emitting state Hidden Markov Models (HMM) for tri-phone based acoustic models. Based on experimental analysis of about 7 hours of training speech data, the acoustic model is best using continuous observation’s probability model of 16 Gaussian mixture distributions and the state distributions were tied to 500 senones. The language model contains both bi-grams and tri-grams. For similar speakers but different sentences, the system obtained a word recognition accuracy of 92.67% and 93.88% and a Word Error Rate (WER) of 11.27% and 10.07% with and without diacritical marks respectively. For different speakers with similar sentences, the system obtained a word recognition accuracy of 95.92% and 96.29% and a WER of 5.78% and 5.45% with and without diacritical marks respectively. Whereas different speakers and different sentences, the system obtained a word recognition accuracy of 89.08% and 90.23% and a WER of 15.59% and 14.44% with and without diacritical marks respectively