Vertigo is a type of dizziness characterised by the subjective feeling of movement despite being stationary. One in four individuals in the community experience symptoms of dizziness at any given time, and it can be challenging for clinicians to diagnose the underlying cause. When dizziness is the result of a malfunction in the inner-ear, the eyes flicker and this is called nystagmus. In this article we describe the first use of Deep Neural Network architectures applied to detecting nystagmus. The data used in these experiments was gathered during a clinical investigation of a novel medical device for recording head and eye movements. We describe methods for training networks using very limited amounts of training data, with an average of 11 mins of nystagmus across four subjects, and less than 24 hours of data in total, per subject. Our methods work by replicating and modifying existing samples to generate new data. In a cross-fold validation experiment, we achieve an average F1 score of 0.59 (SD = 0.24) across all four folds, showing that the methods employed are capable of identifying periods of nystagmus with a modest degree of accuracy. Notably, we were also able to identify periods of pathological nystagmus produced by a patient during an acute attack of Ménière's Disease, despite training the network on nystagmus that was induced by different means.

Automatic visual language identification (VLID) is the technology of using information derived from the visual appearance and movement of the speech articulators to identify 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 discriminating 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 artifacts, 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 30 s of visual speech.

We describe experiments in visual-only language identification (VLID), in which only lip shape, appearance and motion are used to determine the language of a spoken utterance. In previous work, we had shown that this is possible in speaker-dependent mode, i.e. identifying the language spoken by a multi-lingual speaker. Here, by appropriately modifying techniques that have been successful in audio language identification, we extend the work to discriminating two languages in speaker-independent mode. Our results indicate that even with viseme accuracy as low as about 34%, reasonable discrimination can be obtained. A simulation of degraded accuracy viseme recognition performance indicates that high VLID accuracy should be achievable with viseme recognition errors of the order of 50%.

We describe experiments in visual-only language identification, in which only lip-shape and lip-motion are used to determine the language of a spoken utterance. We focus on the task of discriminating between two or three languages spoken by the same speaker, and we have recorded a suitable database for these experiments. We use a standard audio language identification approach in which the feature vectors are tokenized and then a language model for each language is estimated over a stream of tokens. Although rate of speaking appeared to affect our results, it was found that different languages spoken at rather similar speeds were as well discriminated as a single language spoken at three extreme speeds, indicating that there is a language effect present in our results.