Sigma-lognormal modeling of speech

Human movement studies and analyses have been fundamental in many scientific domains, ranging from neuroscience to education, pattern recognition to robotics, health care to sports, and beyond. Previous speech motor models were proposed to understand how speech movement is produced and how the resulting speech varies when some parameters are changed. However, the inverse approach, in which the muscular response parameters and the subject's age are derived from real continuous speech, is not possible with such models. Instead, in the handwriting field, the kinematic theory of rapid human movements and its associated Sigma-lognormal model have been applied successfully to obtain the muscular response parameters. This work presents a speech kinematics based model that can be used to study, analyze, and reconstruct complex speech kinematics in a simplified manner. A method based on the kinematic theory of rapid human movements and its associated Sigma lognormal model are applied to describe and to parameterize the asymptotic impulse response of the neuromuscular networks involved in speech as a response to a neuromotor command. The method used to carry out transformations from formants to a movement observation is also presented. Experiments carried out with the (English) VTR TIMIT database and the (German) Saarbrucken Voice Database, including people of different ages, with and without laryngeal pathologies, corroborate the link between the extracted parameters and aging, on the one hand, and the proportion between the first and second formants required in applying the kinematic theory of rapid human movements, on the other. The results should drive innovative developments in the modeling and understanding of speech kinematics.Comment: Published in Open Acce

Learning and Production of Movement Sequences: Behavioral, Neurophysiological, and Modeling Perspectives

A growing wave of behavioral studies, using a wide variety of paradigms that were introduced or greatly refined in recent years, has generated a new wealth of parametric observations about serial order behavior. What was a mere trickle of neurophysiological studies has grown to a more steady stream of probes of neural sites and mechanisms underlying sequential behavior. Moreover, simulation models of serial behavior generation have begun to open a channel to link cellular dynamics with cognitive and behavioral dynamics. Here we summarize the major results from prominent sequence learning and performance tasks, namely immediate serial recall, typing, 2XN, discrete sequence production, and serial reaction time. These populate a continuum from higher to lower degrees of internal control of sequential organization. The main movement classes covered are speech and keypressing, both involving small amplitude movements that are very amenable to parametric study. A brief synopsis of classes of serial order models, vis-à-vis the detailing of major effects found in the behavioral data, leads to a focus on competitive queuing (CQ) models. Recently, the many behavioral predictive successes of CQ models have been joined by successful prediction of distinctively patterend electrophysiological recordings in prefrontal cortex, wherein parallel activation dynamics of multiple neural ensembles strikingly matches the parallel dynamics predicted by CQ theory. An extended CQ simulation model-the N-STREAMS neural network model-is then examined to highlight issues in ongoing attemptes to accomodate a broader range of behavioral and neurophysiological data within a CQ-consistent theory. Important contemporary issues such as the nature of working memory representations for sequential behavior, and the development and role of chunks in hierarchial control are prominent throughout.Defense Advanced Research Projects Agency/Office of Naval Research (N00014-95-1-0409); National Institute of Mental Health (R01 DC02852

Studies on the impact of assistive communication devices on the quality of life of patients with amyotrophic lateral sclerosis

Tese de doutoramento, Ciências Biomédicas (Neurociências), Universidade de Lisboa, Faculdade de Medicina, 2016Amyotrophic Lateral Sclerosis (ALS) is a progressive neuromuscular disease with rapid and generalized degeneration of motor neurons. Patients with ALS experiment a relentless decline in functions that affect performance of most activities of daily living (ADL), such as speaking, eating, walking or writing. For this reason, dependence on caregivers grows as the disease progresses. Management of the respiratory system is one of the main concerns of medical support, since respiratory failure is the most common cause of death in ALS. Due to increasing muscle weakness, most patients experience dramatic decrease of speech intelligibility and difficulties in using upper limbs (UL) for writing. There is growing evidence that mild cognitive impairment is common in ALS, but most patients are self-conscious of their difficulties in communicating and, in very severe stages, locked-in syndrome can occur. When no other resources than speech and writing are used to assist communication, patients are deprived of expressing needs or feelings, making decisions and keeping social relationships. Further, caregivers feel increased dependence due to difficulties in communication with others and get frustrated about difficulties in understanding partners’ needs. Support for communication is then very important to improve quality of life of both patients and caregivers; however, this has been poorly investigated in ALS. Assistive communication devices (ACD) can support patients by providing a diversity of tools for communication, as they progressively lose speech. ALS, in common with other degenerative conditions, introduces an additional challenge for the field of ACD: as the disease progresses, technologies must adapt to different conditions of the user. In early stages, patients may need speech synthesis in a mobile device, if dysarthria is one of the initial symptoms, or keyboard modifications, as weakness in UL increases. When upper limbs’ dysfunction is high, different input technologies may be adapted to capture voluntary control (for example, eye-tracking devices). Despite the enormous advances in the field of Assistive Technologies, in the last decade, difficulties in clinical support for the use of assistive communication devices (ACD) persist. Among the main reasons for these difficulties are lack of assessment tools to evaluate communication needs and determine proper input devices and to indicate changes over disease progression, and absence of clinical evidence that ACD has relevant impact on the quality of life of affected patients. For this set of reasons, support with communication tools is delayed to stages where patients are severely disabled. Often in these stages, patients face additional clinical complications and increased dependence on their caregivers’ decisions, which increase the difficulty in adaptation to new communication tools. This thesis addresses the role of assistive technologies in the quality of life of early-affected patients with ALS. Also, it includes the study of assessment tools that can improve longitudinal evaluation of communication needs of patients with ALS. We longitudinally evaluated a group of 30 patients with bulbar-onset ALS and 17 caregivers, during 2 to 29 months. Patients were assessed during their regular clinical appointments, in the Hospital de Santa Maria-Centro Hospitalar Lisboa_Norte. Evaluation of patients was based on validated instruments for assessing the Quality of Life (QoL) of patients and caregivers, and on methodologies for recording communication and measuring its performance (including speech, handwriting and typing). We tested the impact of early support with ACD on the QoL of patients with ALS, using a randomized, prospective, longitudinal design. Patients were able to learn and improve their skills to use communication tools based on electronic assistive devices. We found a positive impact of ACD in psychological and wellbeing domains of quality of life in patients, as well as in the support and psychological domains in caregivers. We also studied performance of communication (words per minute) using UL. Performance in handwriting may decline faster than performance in typing, supporting the idea that the use of touchscreen-based ACD supports communication for longer than handwriting. From longitudinal recordings of speech and typing activity we could observe that ACD can support tools to detect early markers of bulbar and UL dysfunction in ALS. Methodologies that were used in this research for recording and assessing function in communication can be replicated in the home environment and form part of the original contributions of this research. Implementation of remote monitoring tools in daily use of ACD, based on these methodologies, is discussed. Considering those patients who receive late support for the use of ACD, lack of time or daily support to learn how to control complex input devices may hinder its use. We developed a novel device to explore the detection and control of various residual movements, based on sensors of accelerometry, electromyography and force, as input signals for communication. The aim of this input device was to develop a tool to explore new communication channels in patients with generalized muscle weakness. This research contributed with novel tools from the Engineering field to the study of assistive communication in patients with ALS. Methodologies that were developed in this work can be further applied to the study of the impact of ACD in other neurodegenerative diseases that affect speech and motor control of UL

Intelligent Advanced User Interfaces for Monitoring Mental Health Wellbeing

It has become pressing to develop objective and automatic measurements integrated in intelligent diagnostic tools for detecting and monitoring depressive states and enabling an increased precision of diagnoses and clinical decision-makings. The challenge is to exploit behavioral and physiological biomarkers and develop Artificial Intelligent (AI) models able to extract information from a complex combination of signals considered key symptoms. The proposed AI models should be able to help clinicians to rapidly formulate accurate diagnoses and suggest personalized intervention plans ranging from coaching activities (exploiting for example serious games), support networks (via chats, or social networks), and alerts to caregivers, doctors, and care control centers, reducing the considerable burden on national health care institutions in terms of medical, and social costs associated to depression cares

Presurgical language fMRI: Mapping of six critical regions.

Language mapping is a key goal in neurosurgical planning. fMRI mapping typically proceeds with a focus on Broca's and Wernicke's areas, although multiple other language-critical areas are now well-known. We evaluated whether clinicians could use a novel approach, including clinician-driven individualized thresholding, to reliably identify six language regions, including Broca's Area, Wernicke's Area (inferior, superior), Exner's Area, Supplementary Speech Area, Angular Gyrus, and Basal Temporal Language Area. We studied 22 epilepsy and tumor patients who received Wada and fMRI (age 36.4[12.5]; Wada language left/right/mixed in 18/3/1). fMRI tasks (two × three tasks) were analyzed by two clinical neuropsychologists who flexibly thresholded and combined these to identify the six regions. The resulting maps were compared to fixed threshold maps. Clinicians generated maps that overlapped significantly, and were highly consistent, when at least one task came from the same set. Cases diverged when clinicians prioritized different language regions or addressed noise differently. Language laterality closely mirrored Wada data (85% accuracy). Activation consistent with all six language regions was consistently identified. In blind review, three external, independent clinicians rated the individualized fMRI language maps as superior to fixed threshold maps; identified the majority of regions significantly more frequently; and judged language laterality to mirror Wada lateralization more often. These data provide initial validation of a novel, clinician-based approach to localizing language cortex. They also demonstrate clinical fMRI is superior when analyzed by an experienced clinician and that when fMRI data is of low quality judgments of laterality are unreliable and should be withheld. Hum Brain Mapp 38:4239-4255, 2017. © 2017 Wiley Periodicals, Inc

The Challenge of Spoken Language Systems: Research Directions for the Nineties

A spoken language system combines speech recognition, natural language processing and human interface technology. It functions by recognizing the person's words, interpreting the sequence of words to obtain a meaning in terms of the application, and providing an appropriate response back to the user. Potential applications of spoken language systems range from simple tasks, such as retrieving information from an existing database (traffic reports, airline schedules), to interactive problem solving tasks involving complex planning and reasoning (travel planning, traffic routing), to support for multilingual interactions. We examine eight key areas in which basic research is needed to produce spoken language systems: (1) robust speech recognition; (2) automatic training and adaptation; (3) spontaneous speech; (4) dialogue models; (5) natural language response generation; (6) speech synthesis and speech generation; (7) multilingual systems; and (8) interactive multimodal systems. In each area, we identify key research challenges, the infrastructure needed to support research, and the expected benefits. We conclude by reviewing the need for multidisciplinary research, for development of shared corpora and related resources, for computational support and far rapid communication among researchers. The successful development of this technology will increase accessibility of computers to a wide range of users, will facilitate multinational communication and trade, and will create new research specialties and jobs in this rapidly expanding area

The Challenge of Spoken Language Systems: Research Directions for the Nineties

A spoken language system combines speech recognition, natural language processing and human interface technology. It functions by recognizing the person's words, interpreting the sequence of words to obtain a meaning in terms of the application, and providing an appropriate response back to the user. Potential applications of spoken language systems range from simple tasks, such as retrieving information from an existing database (traffic reports, airline schedules), to interactive problem solving tasks involving complex planning and reasoning (travel planning, traffic routing), to support for multilingual interactions. We examine eight key areas in which basic research is needed to produce spoken language systems: (1) robust speech recognition; (2) automatic training and adaptation; (3) spontaneous speech; (4) dialogue models; (5) natural language response generation; (6) speech synthesis and speech generation; (7) multilingual systems; and (8) interactive multimodal systems. In each area, we identify key research challenges, the infrastructure needed to support research, and the expected benefits. We conclude by reviewing the need for multidisciplinary research, for development of shared corpora and related resources, for computational support and far rapid communication among researchers. The successful development of this technology will increase accessibility of computers to a wide range of users, will facilitate multinational communication and trade, and will create new research specialties and jobs in this rapidly expanding area

Sigma-lognormal modeling of speech

Human movement studies and analyses have been fundamental in many scientific domains, ranging from neuroscience to education, pattern recognition to robotics, health care to sports, and beyond. Previous speech motor models were proposed to understand how speech movement is produced and how the resulting speech varies when some parameters are changed. However, the inverse approach, in which the muscular response parameters and the subject’s age are derived from real continuous speech, is not possible with such models. Instead, in the handwriting field, the kinematic theory of rapid human movements and its associated Sigma-lognormal model have been applied successfully to obtain the muscular response parameters. This work presents a speech kinematics-based model that can be used to study, analyze, and reconstruct complex speech kinematics in a simplified manner. A method based on the kinematic theory of rapid human movements and its associated Sigma-lognormal model are applied to describe and to parameterize the asymptotic impulse response of the neuromuscular networks involved in speech as a response to a neuromotor command. The method used to carry out transformations from formants to a movement observation is also presented. Experiments carried out with the (English) VTR-TIMIT database and the (German) Saarbrucken Voice Database, including people of different ages, with and without laryngeal pathologies, corroborate the link between the extracted parameters and aging, on the one hand, and the proportion between the first and second formants required in applying the kinematic theory of rapid human movements, on the other. The results should drive innovative developments in the modeling and understanding of speech kinematics

Developing an observational rubric of writing: Preliminary reliability and validity evidence

The purpose of this paper is (1) to report on the design of the early writing observational writing rubric designed to observe and describe change over time in the writing of children emerging into conventional literacy (ages 6–7) within an instructional setting and (2) to investigate the initial reliability and validity of the rubric. We used an extant data set that included 52 videos of writing instruction in Reading Recovery lessons (approximately 520 minutes) and pre- and post-intervention test data, for 24 students, taken at multiple time points across a 20-week period. Dependent sample t-tests and HLM were used to ascertain if the rubric was sensitive to change over occasions. We also considered if the scores correlated with external literacy measures. The findings suggest that the rubric has good initial reliability and validity and is a useful tool for researchers to observe and measure change over time as young children write in an instructional setting; further validation work is required for use in other settings