The practice of speech and language processing in China

Several companies are trying push automatic speech recognition and other technologies past their current limitations.

The Role of the Dorsolateral Prefrontal Cortex for Speech and Language Processing

This review article summarizes various functions of the dorsolateral prefrontal cortex (DLPFC) that are related to language processing. To this end, its connectivity with the left-dominant perisylvian language network was considered, as well as its interaction with other functional networks that, directly or indirectly, contribute to language processing. Language-related functions of the DLPFC comprise various aspects of pragmatic processing such as discourse management, integration of prosody, interpretation of nonliteral meanings, inference making, ambiguity resolution, and error repair. Neurophysiologically, the DLPFC seems to be a key region for implementing functional connectivity between the language network and other functional networks, including cortico-cortical as well as subcortical circuits. Considering clinical aspects, damage to the DLPFC causes psychiatric communication deficits rather than typical aphasic language syndromes. Although the number of well-controlled studies on DLPFC language functions is still limited, the DLPFC might be an important target region for the treatment of pragmatic language disorders.

The prehistory of speech and language is revealed in brain damage

The aim of this paper is to develop further the idea that symptoms that emerge in speech and language processing following brain damage can make a contribution to discussions of the early evolution of language. These diverse impairments are called aphasia, and this paper proposes that the recovery of a non-fluent aphasia syndrome following stroke could provide insights into the course of the pre-history of human language evolution. The observable symptoms emerge during recovery, crucially enabled by (dis)inhibition in parallel with a range of impairments in action processing (apraxias), including apraxia of speech. They are underpinned by changes in cortical and subcortical status following brain damage. It is proposed that the observed recovery mimics ontogenic and phylogenic processes in human speech and language. The arguments put forward provide insights tending to support the motor-gestural model of speech and language evolution. This article is part of the theme issue ‘Reconstructing prehistoric languages’.

EdgeAvatar: An Edge Computing System for Building Virtual Beings

Dialogue systems, also known as conversational agents, are computing systems that use algorithms for speech and language processing to engage in conversation with humans or other conversation-capable systems. A chatbot is a conversational agent that has, as its primary goal, to maximize the length of the conversation without any specific targeted task. When a chatbot is embellished with an artistic approach that is meant to evoke an emotional response, then it is called a virtual being. On the other hand, conversational agents that interact with the physical world require the use of specialized hardware to sense and process captured information. In this article we describe EdgeAvatar, a system based on Edge Computing principles for the creation of virtual beings. The objective of the EdgeAvatar system is to provide a streamlined and modular framework for virtual being applications that are to be deployed in public settings. We also present two implementations that use EdgeAvatar and are inspired by historical figures to interact with visitors of the Venice Biennale 2019. EdgeAvatar can adapt to fit different approaches for AI powered conversations.

Developing a Shared Task for Speech Processing on Endangered Languages

Advances in speech and language processing have enabled the creation of applications that could, in principle, accelerate the process of language documentation, as speech communities and linguists work on urgent language documentation and reclamation projects. However, such systems have yet to make a significant impact on language documentation, as resource requirements limit the broad applicability of these new techniques. We aim to exploit the framework of shared tasks to focus the technology research community on tasks which address key pain points in language documentation. Here we present initial steps in the implementation of these new shared tasks, through the creation of data sets drawn from endangered language repositories and baseline systems to perform segmentation and speaker labeling of these audio recordings—important enabling steps in the documentation process. This paper motivates these tasks with a use case, describes data set curation and baseline systems, and presents results on this data. We then highlight the challenges and ethical considerations in developing these speech processing tools and tasks to support endangered language documentation.

Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review

Background: Language is a valuable source of clinical information in Alzheimer’s disease, as it declines concurrently with neurodegeneration. Consequently, speech and language data have been extensively studied in connection with its diagnosis. Objective: Firstly, to summarize the existing findings on the use of artificial intelligence, speech, and language processing to predict cognitive decline in the context of Alzheimer’s disease. Secondly, to detail current research procedures, highlight their limitations, and suggest strategies to address them. Methods: Systematic review of original research between 2000 and 2019, registered in PROSPERO (reference CRD42018116606). An interdisciplinary search covered six databases on engineering (ACM and IEEE), psychology (PsycINFO), medicine (PubMed and Embase), and Web of Science. Bibliographies of relevant papers were screened until December 2019. Results: From 3,654 search results, 51 articles were selected against the eligibility criteria. Four tables summarize their findings: study details (aim, population, interventions, comparisons, methods, and outcomes), data details (size, type, modalities, annotation, balance, availability, and language of study), methodology (pre-processing, feature generation, machine learning, evaluation, and results), and clinical applicability (research implications, clinical potential, risk of bias, and strengths/limitations). Conclusion: Promising results are reported across nearly all 51 studies, but very few have been implemented in clinical research or practice. The main limitations of the field are poor standardization, limited comparability of results, and a degree of disconnect between study aims and clinical applications. Active attempts to close these gaps will support translation of future research into clinical practice.

Psychophysiological and Eye-Tracking Markers of Speech and Language Processing in Neurodevelopmental Disorders: New Options for Difficult-to-Test Populations

It can be challenging to accurately assess speech and language processing in preverbal or minimally verbal individuals with neurodevelopmental disabilities (NDD) using standardized behavioral tools. Event-related potential and eye tracking methods offer novel means to objectively document receptive language processing without requiring purposeful behavioral responses. Working around many of the cognitive, motor, or social difficulties in NDDs, these tools allow for minimally invasive, passive assessment of language processing and generate continuous scores that may have utility as biomarkers of individual differences and indicators of treatment effectiveness. Researchers should consider including physiological measures in assessment batteries to allow for more precise capture of language processing in individuals for whom it may not behaviorally apparent.