A speech sample can be morphed from one voice to another by computing a sufficiently detailed time-frequency parameterization of the signal, optionally decomposing it into speaker-dependent and speaker-independent signal components, editing the parameterization or signal components, and then generating a speech signal based on the edited parameterization. Fine-grained control of time-frequency parameters has found applications in the scientific study of speech perception and in artistic applications such as voiceover and music. Coarse-grained control of speaker identity, emotion, and second-language accent have been applied to various tasks, including the training of speech recognizers and speaker anonymization.
Singular they is valuable to psycholinguistic research because it has a long-standing usage in English that is currently undergoing changes, both in how often it is used and in what conditions. As a result, singular they is an informative testing ground for the cognitive mechanisms behind language change, which include both top-down intentional change and implicit learning and adaptation. Singular they is also an unusually socially salient function word that provides opportunities to examine how automatic psycholinguistic processes intersect with sociolinguistic issues like gender-fair language, linguistic prescriptivism, and the emerging use of they as a personal pronoun. Developments in the use of singular they are linked to broader shifts in concepts of gender. We suggest that there are two main varieties of singular they that currently differ in acceptability and processing characteristics. Psycholinguistic research shows that singular they is viewed as more acceptable and used more easily within a supportive context that helps to make the pronoun pragmatically appropriate.
Sign language machine translation has progressed substantially over the past decade, evolving from isolated sign recognition to end-to-end translation systems. Advances in pose estimation, transformer architectures, and large-scale dataset collection have driven progress, yet challenges remain. Datasets are limited compared to spoken-language resources; evaluation metrics inadequately capture the linguistic quality of output; and models must capture the simultaneous, multi-layered, and three-dimensional structure of sign languages. This manuscript provides a comprehensive review that seeks to balance technical challenges with stakeholder considerations. We examine the linguistic properties that make sign languages computationally unique, trace the evolution of recognition, translation, and production systems, and analyze ongoing technical challenges. Crucially, we address ethical considerations around data governance, community involvement, and appropriate use. Drawing on interdisciplinary perspectives spanning computer vision, sign language linguistics, and deaf studies, our analysis emphasizes that continued progress requires sustained collaboration across these fields and with deaf communities.
While traditional semantic accounts of demonstratives assume spatial contrast to be fundamental, typological studies reveal that the encoded meanings go well beyond physical distance and involve contrasts in perceptual and epistemic access. This article reviews the main findings from typological studies and the implications they have for the semantic analysis of demonstratives. The typological picture suggests that a uniformly applicable analysis puts very little into the lexical meaning of demonstratives, allowing for contextual information to determine their denotation differently across contexts and languages. By keeping the inherent semantics of demonstratives minimal and allowing language-specific conventionalizations of contextual information, we can bridge the gap between the typological observations and formal semantic theories of demonstratives.
In this era of rapid artificial intelligence (AI) expansion, computational approaches are reshaping methods for language documentation and description. We survey the history of computational methods that have been applied to research in languages with limited digital resources and also present cutting-edge methods, such as large language models (LLMs), that have the potential to benefit documentary and descriptive fieldwork. We highlight how these methods affect data collection and annotation, transcription and phonological analysis, morphosyntactic description, and translation. Linguists, natural language processing engineers, and speech communities must consider how the use of computational methods such as data mining and machine learning should influence ethical best practices in linguistic field methods and how communities can continue to guide the documentation and maintenance of their languages in the age of AI. Looking forward, LLMs and making computational methods broadly usable through user interfaces are likely to emerge as prominent themes in documentary and descriptive research.
