Individual differences in the discrimination of novel speech sounds: effects of sex, temporal processing, musical and cognitive abilities

This study examined whether rapid temporal auditory processing, verbal working memory capacity, non-verbal intelligence, executive functioning, musical ability and prior foreign language experience predicted how well native English speakers (N = 120) discriminated Norwegian tonal and vowel contrasts as well as a non-speech analogue of the tonal contrast and a native vowel contrast presented over noise. Results confirmed a male advantage for temporal and tonal processing, and also revealed that temporal processing was associated with both non-verbal intelligence and speech processing. In contrast, effects of musical ability on non-native speech-sound processing and of inhibitory control on vowel discrimination were not mediated by temporal processing. These results suggest that individual differences in non-native speech-sound processing are to some extent determined by temporal auditory processing ability, in which males perform better, but are also determined by a host of other abilities that are deployed flexibly depending on the characteristics of the target sounds

EdgeFool: An Adversarial Image Enhancement Filter

Adversarial examples are intentionally perturbed images that mislead classifiers. These images can, however, be easily detected using denoising algorithms, when high-frequency spatial perturbations are used, or can be noticed by humans, when perturbations are large. In this paper, we propose EdgeFool, an adversarial image enhancement filter that learns structure-aware adversarial perturbations. EdgeFool generates adversarial images with perturbations that enhance image details via training a fully convolutional neural network end-to-end with a multi-task loss function. This loss function accounts for both image detail enhancement and class misleading objectives. We evaluate EdgeFool on three classifiers (ResNet-50, ResNet-18 and AlexNet) using two datasets (ImageNet and Private-Places365) and compare it with six adversarial methods (DeepFool, SparseFool, Carlini-Wagner, SemanticAdv, Non-targeted and Private Fast Gradient Sign Methods)

Does anticipation help or hinder performance in a subsequent speech?

This study examined the effects of anticipatory processing on a subsequent speech in high and low socially anxious participants. Forty participants (n = 20 in each group) gave two speeches, one after no anticipatory processing and one after 10-minutes of anticipatory processing. In anticipatory processing, high socially anxious participants were more anxious, and experienced more negative and unhelpful self-images than low socially anxious participants did. However, both groups rated memories of past speeches as having a somewhat helpful effect on their speech preparation. High socially anxious participants tended to use the observer perspective more in the anticipated speech, while, in the unanticipated speech, they might have been switching between observer and field perspectives. Low socially anxious participants tended to use the field perspective in both speeches. High and low socially anxious participants reported better speech performances after the anticipated, compared to after the unanticipated speech. Results suggest that anticipatory processing may have both positive and negative effects on socially anxious individuals' cognitive processing and performance before and during a speech

Transfer Effect of Speech-sound Learning on Auditory-motor Processing of Perceived Vocal Pitch Errors

Speech perception and production are intimately linked. There is evidence that speech motor learning results in changes to auditory processing of speech. Whether speech motor control benefits from perceptual learning in speech, however, remains unclear. This event-related potential study investigated whether speech-sound learning can modulate the processing of feedback errors during vocal pitch regulation. Mandarin speakers were trained to perceive five Thai lexical tones while learning to associate pictures with spoken words over 5 days. Before and after training, participants produced sustained vowel sounds while they heard their vocal pitch feedback unexpectedly perturbed. As compared to the pre-training session, the magnitude of vocal compensation significantly decreased for the control group, but remained consistent for the trained group at the post-training session. However, the trained group had smaller and faster N1 responses to pitch perturbations and exhibited enhanced P2 responses that correlated significantly with their learning performance. These findings indicate that the cortical processing of vocal pitch regulation can be shaped by learning new speech-sound associations, suggesting that perceptual learning in speech can produce transfer effects to facilitating the neural mechanisms underlying the online monitoring of auditory feedback regarding vocal production

Speech systems research at Texas Instruments

An assessment of automatic speech processing technology is presented. Fundamental problems in the development and the deployment of automatic speech processing systems are defined and a technology forecast for speech systems is presented

FIT A Fog Computing Device for Speech TeleTreatments

There is an increasing demand for smart fogcomputing gateways as the size of cloud data is growing. This paper presents a Fog computing interface (FIT) for processing clinical speech data. FIT builds upon our previous work on EchoWear, a wearable technology that validated the use of smartwatches for collecting clinical speech data from patients with Parkinson's disease (PD). The fog interface is a low-power embedded system that acts as a smart interface between the smartwatch and the cloud. It collects, stores, and processes the speech data before sending speech features to secure cloud storage. We developed and validated a working prototype of FIT that enabled remote processing of clinical speech data to get speech clinical features such as loudness, short-time energy, zero-crossing rate, and spectral centroid. We used speech data from six patients with PD in their homes for validating FIT. Our results showed the efficacy of FIT as a Fog interface to translate the clinical speech processing chain (CLIP) from a cloud-based backend to a fog-based smart gateway.Comment: 3 pages, 5 figures, 1 table, 2nd IEEE International Conference on Smart Computing SMARTCOMP 2016, Missouri, USA, 201