Neural dynamics of selective attention to speech in noise

This thesis investigates how the neural system instantiates selective attention to speech in challenging acoustic conditions, such as spectral degradation and the presence of background noise. Four studies using behavioural measures, magneto- and electroencephalography (M/EEG) recordings were conducted in younger (20–30 years) and older participants (60–80 years). The overall results can be summarized as follows. An EEG experiment demonstrated that slow negative potentials reflect participants’ enhanced allocation of attention when they are faced with more degraded acoustics. This basic mechanism of attention allocation was preserved at an older age. A follow-up experiment in younger listeners indicated that attention allocation can be further enhanced in a context of increased task-relevance through monetary incentives. A subsequent study focused on brain oscillatory dynamics in a demanding speech comprehension task. The power of neural alpha oscillations (~10 Hz) reflected a decrease in demands on attention with increasing acoustic detail and critically also with increasing predictiveness of the upcoming speech content. Older listeners’ behavioural responses and alpha power dynamics were stronger affected by acoustic detail compared with younger listeners, indicating that selective attention at an older age is particularly dependent on the sensory input signal. An additional analysis of listeners’ neural phase-locking to the temporal envelopes of attended speech and unattended background speech revealed that younger and older listeners show a similar segregation of attended and unattended speech on a neural level. A dichotic listening experiment in the MEG aimed at investigating how neural alpha oscillations support selective attention to speech. Lateralized alpha power modulations in parietal and auditory cortex regions predicted listeners’ focus of attention (i.e., left vs right). This suggests that alpha oscillations implement an attentional filter mechanism to enhance the signal and to suppress noise. A final behavioural study asked whether acoustic and semantic aspects of task-irrelevant speech determine how much it interferes with attention to task-relevant speech. Results demonstrated that younger and older adults were more distracted when acoustic detail of irrelevant speech was enhanced, whereas predictiveness of irrelevant speech had no effect. All findings of this thesis are integrated in an initial framework for the role of attention for speech comprehension under demanding acoustic conditions

Aerospace medicine and biology: A continuing bibliography with indexes

This bibliography lists 138 reports, articles, and other documents introduced into the NASA scientific and technical information system in Jun. 1980

Decoding auditory attention and neural language processing in adverse conditions and different listener groups

This thesis investigated subjective, behavioural and neurophysiological (EEG) measures of speech processing in various adverse conditions and with different listener groups. In particular, this thesis focused on different neural processing stages and their relationship with auditory attention, effort, and measures of speech intelligibility. Study 1 set the groundwork by establishing a toolbox of various neural measures to investigate online speech processing, from the frequency following response (FFR) and cortical measures of speech processing, to the N400, a measure of lexico-semantic processing. Results showed that peripheral processing is heavily influenced by stimulus characteristics such as degradation, whereas central processing units are more closely linked to higher-order phenomena such as speech intelligibility. In Study 2, a similar experimental paradigm was used to investigate differences in neural processing between a hearing-impaired and a normal-hearing group. Subjects were presented with short stories in different levels of multi-talker babble noise, and with different settings on their hearing aids. Findings indicate that, particularly at lower noise levels, the hearing-impaired group showed much higher cortical entrainment than the normal- hearing group, despite similar levels of speech recognition. Intersubject correlation, another global neural measure of auditory attention, however, was similarly affected by noise levels in both the hearing-impaired and the normal-hearing group. This finding indicates extra processing in the hearing-impaired group only on the level of the auditory cortex. Study 3, in contrast to Studies 1 and 2 (which both investigated the effects of bottom-up factors on neural processing), examined the links between entrainment and top-down factors, specifically motivation; as well as reasons for the 5 higher entrainment found in hearing-impaired subjects in Study 2. Results indicated that, while behaviourally there was no difference between incentive and non-incentive conditions, neurophysiological measures of attention such as intersubject correlation were affected by the presence of an incentive to perform better. Moreover, using a specific degradation type resulted in subjects’ increased cortical entrainment under degraded conditions. These findings support the hypothesis that top-down factors such as motivation influence neurophysiological measures; and that higher entrainment to degraded speech might be triggered specifically by the reduced availability of spectral detail contained in speech

The role of zero crossings in speech recognition and processing

Imperial Users onl

Golden oldies and silver brains : Deficits, preservation, learning, and rehabilitation effects of music in ageing-related neurological disorders

During the last decades, there have been major advances in mapping the brain regions that underlie our ability to perceive, experience, and produce music and how musical training can shape the structure and function of the brain. This progress has fueled and renewed clinical interest towards uncovering the neural basis for the impaired or preserved processing of music in different neurological disorders and how music-based interventions can be used in their rehabilitation and care. This article reviews our contribution to and the state-of-the-art of this field. We will provide a short overview outlining the key brain networks that participate in the processing of music and singing in the healthy brain and then present recent findings on the following key music-related research topics in neurological disorders: (i) the neural architecture underlying deficient processing of music (amusia), (ii) the preservation of singing in aphasia and music-evoked emotions and memories in Alzheimer's disease, (iii) the mnemonic impact of songs as a verbal learning tool, and (iv) the cognitive, emotional, and neural efficacy of music-based interventions and activities in the rehabilitation and care of major ageing-related neurological illnesses (stroke, Alzheimer's disease, and Parkinson's disease). (C) 2018 Elsevier Ltd. All rights reserved.Peer reviewe

Predicting and auralizing acoustics in classrooms

Although classrooms have fairly simple geometries, this type of room is known to cause problems when trying to predict their acoustics using room acoustics computer modeling. Some typical features from a room acoustics point of view are: Parallel walls, low ceilings (the rooms are flat), uneven distribution of absorption, and most of the floor being covered with furniture which at long distances act as scattering elements, and at short distance provide strong specular components. The importance of diffraction and scattering is illustrated in numbers and by means of auralization, using ODEON 8 Beta

Learning-Based Reference-Free Speech Quality Assessment for Normal Hearing and Hearing Impaired Applications

Accurate speech quality measures are highly attractive and beneficial in the design, fine-tuning, and benchmarking of speech processing algorithms, devices, and communication systems. Switching from narrowband telecommunication to wideband telephony is a change within the telecommunication industry which provides users with better speech quality experience but introduces a number of challenges in speech processing. Noise is the most common distortion on audio signals and as a result there have been a lot of studies on developing high performance noise reduction algorithms. Assistive hearing devices are designed to decrease communication difficulties for people with loss of hearing. As the algorithms within these devices become more advanced, it becomes increasingly crucial to develop accurate and robust quality metrics to assess their performance. Objective speech quality measurements are more attractive compared to subjective assessments as they are cost-effective and subjective variability is eliminated. Although there has been extensive research on objective speech quality evaluation for narrowband speech, those methods are unsuitable for wideband telephony. In the case of hearing-impaired applications, objective quality assessment is challenging as it has to be capable of distinguishing between desired modifications which make signals audible and undesired artifacts. In this thesis a model is proposed that allows extracting two sets of features from the distorted signal only. This approach which is called reference-free (nonintrusive) assessment is attractive as it does not need access to the reference signal. Although this benefit makes nonintrusive assessments suitable for real-time applications, more features need to be extracted and smartly combined to provide comparable accuracy as intrusive metrics. Two feature vectors are proposed to extract information from distorted signals and their performance is examined in three studies. In the first study, both feature vectors are trained on various portions of a noise reduction database for normal hearing applications. In the second study, the same investigation is performed on two sets of databases acquired through several hearing aids. Third study examined the generalizability of the proposed metrics on benchmarking four wireless remote microphones in a variety of environmental conditions. Machine learning techniques are deployed for training the models in the three studies. The studies show that one of the feature sets is robust when trained on different portions of the data from different databases and it also provides good quality prediction accuracy for both normal hearing and hearing-impaired applications