Publications
Investigation of the relationship of vocal, eye-tracking, and fMRI ROI time-series measures with preclinical mild traumatic brain injury
October 19, 2019
Abstract
Published in:
Neuroscience 2019
Summary
In this work, we are examining correlations between vocal articulatory features, ocular smooth pursuit measures, and features from the fMRI BOLD response in regions of interest (ROI) time series in a high school athlete population susceptible to repeated head impact within a sports season. Initial results have indicated relationships between vocal features and brain ROIs that may show which components of the neural speech networks effected are effected by preclinical mild traumatic brain injury (mTBI). The data used for this study was collected by Purdue University on 32 high school athletes over the entirety of a sports season (Helfer, et al., 2014), and includes fMRI measurements made pre-season, in-season, and postseason. The athletes are 25 male football players and 7 female soccer players. The Immediate Post-Concussion Assessment and Cognitive Testing suite (ImPACT) was used as a means of assessing cognitive performance (Broglio, Ferrara, Macciocchi, Baumgartner, & Elliott, 2007). The test is made up of six sections, which measure verbal memory, visual memory, visual motor speed, reaction time, impulse control, and a total symptom composite. Using each test, a threshold is set for a change in cognitive performance. The threshold for each test is defined as a decline from baseline that exceeds one standard deviation, where the standard deviation is computed over the change from baseline across all subjects’ test scores. Speech features were extracted from audio recordings of the Grandfather Passage, which provides a standardized and phonetically balanced sample of speech. Oculomotor testing included two experimental conditions. In the smooth pursuit condition, a single target moving circularly, at constant speed. In the saccade condition, a target was jumped between one of three location along the horizontal midline of the screen. In both trial types, subjects visually tracked the targets during the trials, which lasted for one minute. The fMRI features are derived from the bold time-series data from resting state fMRI scans of the subjects. The pre-processing of the resting state fMRI and accompanying structural MRI data (for Atlas registration) was performed with the toolkit CONN (Whitfield-Gabrieli & Nieto-Castanon, 2012). Functional connectivity was generated using cortical and sub-cortical atlas registrations. This investigation will explores correlations between these three modalities and a cognitive performance assessment.
Summary
In this work, we are examining correlations between vocal articulatory features, ocular smooth pursuit measures, and features from the fMRI BOLD response in regions of interest (ROI) time series in a high school athlete population susceptible to repeated head impact within a sports season. Initial results have indicated relationships between...
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Comparison of two-talker attention decoding from EEG with nonlinear neural networks and linear methods
August 8, 2019
Conference Paper
Published in:
Sci. Rep., Vol. 9, No. 1, 8 August 2019, 11538.
R&D area:
R&D group:
Summary
Auditory attention decoding (AAD) through a brain-computer interface has had a flowering of developments since it was first introduced by Mesgarani and Chang (2012) using electrocorticograph recordings. AAD has been pursued for its potential application to hearing-aid design in which an attention-guided algorithm selects, from multiple competing acoustic sources, which should be enhanced for the listener and which should be suppressed. Traditionally, researchers have separated the AAD problem into two stages: reconstruction of a representation of the attended audio from neural signals, followed by determining the similarity between the candidate audio streams and the reconstruction. Here, we compare the traditional two-stage approach with a novel neural-network architecture that subsumes the explicit similarity step. We compare this new architecture against linear and non-linear (neural-network) baselines using both wet and dry electroencephalogram (EEG) systems. Our results indicate that the new architecture outperforms the baseline linear stimulus-reconstruction method, improving decoding accuracy from 66% to 81% using wet EEG and from 59% to 87% for dry EEG. Also of note was the finding that the dry EEG system can deliver comparable or even better results than the wet, despite the latter having one third as many EEG channels as the former. The 11-subject, wet-electrode AAD dataset for two competing, co-located talkers, the 11-subject, dry-electrode AAD dataset, and our software are available for further validation, experimentation, and modification.
Summary
Auditory attention decoding (AAD) through a brain-computer interface has had a flowering of developments since it was first introduced by Mesgarani and Chang (2012) using electrocorticograph recordings. AAD has been pursued for its potential application to hearing-aid design in which an attention-guided algorithm selects, from multiple competing acoustic sources, which...
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Detecting depression using vocal, facial and semantic communication cues
October 15, 2016
Conference Paper
Published in:
ACM Multimedia Conf. AVEC Workshop, 15-19 October 2016.
Summary
Major depressive disorder (MDD) is known to result in neurophysiological and neurocognitive changes that affect control of motor, linguistic, and cognitive functions. MDD's impact on these processes is reflected in an individual's communication via coupled mechanisms: vocal articulation, facial gesturing and choice of content to convey in a dialogue. In particular, MDD-induced neurophysiological changes are associated with a decline in dynamics and coordination of speech and facial motor control, while neurocognitive changes influence dialogue semantics. In this paper, biomarkers are derived from all of these modalities, drawing first from previously developed neurophysiologically motivated speech and facial coordination and timing features. In addition, a novel indicator of lower vocal tract constriction in articulation is incorporated that relates to vocal projection. Semantic features are analyzed for subject/avatar dialogue content using a sparse coded lexical embedding space, and for contextual clues related to the subject's present or past depression status. The features and depression classification system were developed for the 6th International Audio/Video Emotion Challenge (AVEC), which provides data consisting of audio, video-based facial action units, and transcribed text of individuals communicating with the human-controlled avatar. A clinical Patient Health Questionnaire (PHQ) score and binary depression decision are provided for each participant. PHQ predictions were obtained by fusing outputs from a Gaussian staircase regressor for each feature set, with results on the development set of mean F1=0.81, RMSE=5.31, and MAE=3.34. These compare favorably to the challenge baseline development results of mean F1=0.73, RMSE=6.62, and MAE=5.52. On test set evaluation, our system obtained a mean F1=0.70, which is similar to the challenge baseline test result. Future work calls for consideration of joint feature analyses across modalities in an effort to detect neurological disorders based on the interplay of motor, linguistic, affective, and cognitive components of communication.
Summary
Major depressive disorder (MDD) is known to result in neurophysiological and neurocognitive changes that affect control of motor, linguistic, and cognitive functions. MDD's impact on these processes is reflected in an individual's communication via coupled mechanisms: vocal articulation, facial gesturing and choice of content to convey in a dialogue. In...
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Relation of automatically extracted formant trajectories with intelligibility loss and speaking rate decline in amyotrophic lateral sclerosis
September 8, 2016
Conference Paper
Published in:
INTERSPEECH 2016: 16th Annual Conf. of the Int. Speech Communication Assoc., 8-12 September 2016.
Summary
Effective monitoring of bulbar disease progression in persons with amyotrophic lateral sclerosis (ALS) requires rapid, objective, automatic assessment of speech loss. The purpose of this work was to identify acoustic features that aid in predicting intelligibility loss and speaking rate decline in individuals with ALS. Features were derived from statistics of the first (F1) and second (F2) formant frequency trajectories and their first and second derivatives. Motivated by a possible link between components of formant dynamics and specific articulator movements, these features were also computed for low-pass and high-pass filtered formant trajectories. When compared to clinician-rated intelligibility and speaking rate assessments, F2 features, particularly mean F2 speed and a novel feature, mean F2 acceleration, were most strongly correlated with intelligibility and speaking rate, respectively (Spearman correlations > 0.70, p < 0.0001). These features also yielded the best predictions in regression experiments (r > 0.60, p < 0.0001). Comparable results were achieved using low-pass filtered F2 trajectory features, with higher correlations and lower prediction errors achieved for speaking rate over intelligibility. These findings suggest information can be exploited in specific frequency components of formant trajectories, with implications for automatic monitoring of ALS.
Summary
Effective monitoring of bulbar disease progression in persons with amyotrophic lateral sclerosis (ALS) requires rapid, objective, automatic assessment of speech loss. The purpose of this work was to identify acoustic features that aid in predicting intelligibility loss and speaking rate decline in individuals with ALS. Features were derived from statistics...
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Relating estimated cyclic spectral peak frequency to measured epilarynx length using magnetic resonance imaging
September 8, 2016
Conference Paper
Published in:
INTERSPEECH 2016: 16th Annual Conf. of the Int. Speech Communication Assoc., 8-12 September 2016.
Summary
The epilarynx plays an important role in speech production, carrying information about the individual speaker and manner of articulation. However, precise acoustic behavior of this lower vocal tract structure is difficult to establish. Focusing on acoustics observable in natural speech, recent spectral processing techniques isolate a unique resonance with characteristics of the epilarynx previously shown via simulation, specifically cyclicity (i.e. energy differences between the closed and open phases of the glottal cycle) in a 3-5kHz region observed across vowels. Using Magnetic Resonance Imaging (MRI), the present work relates this estimated cyclic peak frequency to measured epilarynx length. Assuming a simple quarter wavelength relationship, the cavity length estimated from the cyclic peak frequency is shown to be directly proportional (linear fit slope =1.1) and highly correlated (p = 0.85, pval<10^?4) to the measured epilarynx length across speakers. Results are discussed, as are implications in speech science and application domains.
Summary
The epilarynx plays an important role in speech production, carrying information about the individual speaker and manner of articulation. However, precise acoustic behavior of this lower vocal tract structure is difficult to establish. Focusing on acoustics observable in natural speech, recent spectral processing techniques isolate a unique resonance with characteristics...
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A vocal modulation model with application to predicting depression severity
June 14, 2016
Conference Paper
Published in:
13th IEEE Int. Conf. on Wearable and Implantable Body Sensor Networks, BSN 2016, 14-17 June 2016.
Summary
Speech provides a potential simple and noninvasive "on-body" means to identify and monitor neurological diseases. Here we develop a model for a class of vocal biomarkers exploiting modulations in speech, focusing on Major Depressive Disorder (MDD) as an application area. Two model components contribute to the envelope of the speech waveform: amplitude modulation (AM) from respiratory muscles, and AM from interaction between vocal tract resonances (formants) and frequency modulation in vocal fold harmonics. Based on the model framework, we test three methods to extract envelopes capturing these modulations of the third formant for synthesized sustained vowels. Using subsequent modulation features derived from the model, we predict MDD severity scores with a Gaussian Mixture Model. Performing global optimization over classifier parameters and number of principal components, we evaluate performance of the features by examining the root-mean-squared error (RMSE), mean absolute error (MAE), and Spearman correlation between the actual and predicted MDD scores. We achieved RMSE and MAE values 10.32 and 8.46, respectively (Spearman correlation=0.487, p<0.001), relative to a baseline RMSE of 11.86 and MAE of 10.05, obtained by predicting the mean MDD severity score. Ultimately, our model provides a framework for detecting and monitoring vocal modulations that could also be applied to other neurological diseases.
Summary
Speech provides a potential simple and noninvasive "on-body" means to identify and monitor neurological diseases. Here we develop a model for a class of vocal biomarkers exploiting modulations in speech, focusing on Major Depressive Disorder (MDD) as an application area. Two model components contribute to the envelope of the speech...
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Assessing functional neural connectivity as an indicator of cognitive performance
December 11, 2015
Conference Paper
Published in:
5th NIPS Workshop on Machine Learning and Interpretation in Neuroimaging, MLINI 2015, 11-12 December 2015.
Topic:
R&D area:
R&D group:
Summary
Studies in recent years have demonstrated that neural organization and structure impact an individual's ability to perform a given task. Specifically, individuals with greater neural efficiency have been shown to outperform those with less organized functional structure. In this work, we compare the predictive ability of properties of neural connectivity on a working memory task. We provide two novel approaches for characterizing functional network connectivity from electroencephalography (EEG), and compare these features to the average power across frequency bands in EEG channels. Our first novel approach represents functional connectivity structure through the distribution of eigenvalues making up channel coherence matrices in multiple frequency bands. Our second approach creates a connectivity network at each frequency band, and assesses variability in average path lengths of connected components and degree across the network. Failures in digit and sentence recall on single trials are detected using a Gaussian classifier for each feature set, at each frequency band. The classifier results are then fused across frequency bands, with the resulting detection performance summarized using the area under the receiver operating characteristic curve (AUC) statistic. Fused AUC results of 0.63/0.58/0.61 for digit recall failure and 0.58/0.59/0.54 for sentence recall failure are obtained from the connectivity structure, graph variability, and channel power features respectively.
Summary
Studies in recent years have demonstrated that neural organization and structure impact an individual's ability to perform a given task. Specifically, individuals with greater neural efficiency have been shown to outperform those with less organized functional structure. In this work, we compare the predictive ability of properties of neural connectivity...
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Estimating lower vocal tract features with closed-open phase spectral analyses
September 6, 2015
Conference Paper
Published in:
INTERSPEECH 2015: 15th Annual Conf. of the Int. Speech Communication Assoc., 6-10 September 2015.
Summary
Previous studies have shown that, in addition to being speaker-dependent yet context-independent, lower vocal tract acoustics significantly impact the speech spectrum at mid-to-high frequencies (e.g 3-6kHz). The present work automatically estimates spectral features that exhibit acoustic properties of the lower vocal tract. Specifically aiming to capture the cyclicity property of the epilarynx tube, a novel multi-resolution approach to spectral analyses is presented that exploits significant differences between the closed and open phases of a glottal cycle. A prominent null linked to the piriform fossa is also estimated. Examples of the feature estimation on natural speech of the VOICES multi-speaker corpus illustrate that a salient spectral pattern indeed emerges between 3-6kHz across all speakers. Moreover, the observed pattern is consistent with that canonically shown for the lower vocal tract in previous works. Additionally, an instance of a speaker's formant (i.e. spectral peak around 3kHz that has been well-established as a characteristic of voice projection) is quantified here for the VOICES template speaker in relation to epilarynx acoustics. The corresponding peak is shown to be double the power on average compared to the other speakers (20 vs 10 dB).
Summary
Previous studies have shown that, in addition to being speaker-dependent yet context-independent, lower vocal tract acoustics significantly impact the speech spectrum at mid-to-high frequencies (e.g 3-6kHz). The present work automatically estimates spectral features that exhibit acoustic properties of the lower vocal tract. Specifically aiming to capture the cyclicity property of...
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Speech enhancement using sparse convolutive non-negative matrix factorization with basis adaptation
September 9, 2012
Conference Paper
Published in:
INTERSPEECH 2012: 13th Annual Conf. of the Int. Speech Communication Assoc., 9-13 September 2012.
Summary
We introduce a framework for speech enhancement based on convolutive non-negative matrix factorization that leverages available speech data to enhance arbitrary noisy utterances with no a priori knowledge of the speakers or noise types present. Previous approaches have shown the utility of a sparse reconstruction of the speech-only components of an observed noisy utterance. We demonstrate that an underlying speech representation which, in addition to applying sparsity, also adapts to the noisy acoustics improves overall enhancement quality. The proposed system performs comparably to a traditional Wiener filtering approach, and the results suggest that the proposed framework is most useful in moderate- to low-SNR scenarios.
Summary
We introduce a framework for speech enhancement based on convolutive non-negative matrix factorization that leverages available speech data to enhance arbitrary noisy utterances with no a priori knowledge of the speakers or noise types present. Previous approaches have shown the utility of a sparse reconstruction of the speech-only components of...
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Vocal-source biomarkers for depression - a link to psychomotor activity
September 9, 2012
Conference Paper
Published in:
INTERSPEECH 2012: 13th Annual Conf. of the Int. Speech Communication Assoc., 9-13 September 2012.
Summary
A hypothesis in characterizing human depression is that change in the brain's basal ganglia results in a decline of motor coordination. Such a neuro-physiological change may therefore affect laryngeal control and dynamics. Under this hypothesis, toward the goal of objective monitoring of depression severity, we investigate vocal-source biomarkers for depression; specifically, source features that may relate to precision in motor control, including vocal-fold shimmer and jitter, degree of aspiration, fundamental frequency dynamics, and frequency-dependence of variability and velocity of energy. We use a 35-subject database collected by Mundt et al. in which subjects were treated over a six-week period, and investigate correlation of our features with clinical (HAMD), as well as self-reported (QIDS) Total subject assessment scores. To explicitly address the motor aspect of depression, we compute correlations with the Psychomotor Retardation component of clinical and self-reported Total assessments. For our longitudinal database, most correlations point to statistical relationships of our vocal-source biomarkers with psychomotor activity, as well as with depression severity.
Summary
A hypothesis in characterizing human depression is that change in the brain's basal ganglia results in a decline of motor coordination. Such a neuro-physiological change may therefore affect laryngeal control and dynamics. Under this hypothesis, toward the goal of objective monitoring of depression severity, we investigate vocal-source biomarkers for depression...
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