@article{GarciaDeryRoeseretal.2018,
  author    = {Garcia, Rowena and Dery, Jeruen E. and Roeser, Jens and H{\"o}hle, Barbara},
  title     = {Word order preferences of Tagalog-speaking adults and children},
  series = {First language},
  volume    = {38},
  journal   = {First language},
  number    = {6},
  publisher = {Sage Publ.},
  address   = {London},
  issn      = {0142-7237},
  doi       = {10.1177/0142723718790317},
  pages     = {617 -- 640},
  year      = {2018},
  abstract  = {This article investigates the word order preferences of Tagalog-speaking adults and five- and seven-year-old children. The participants were asked to complete sentences to describe pictures depicting actions between two animate entities. Adults preferred agent-initial constructions in the patient voice but not in the agent voice, while the children produced mainly agent-initial constructions regardless of voice. This agent-initial preference, despite the lack of a close link between the agent and the subject in Tagalog, shows that this word order preference is not merely syntactically-driven (subject-initial preference). Additionally, the children's agent-initial preference in the agent voice, contrary to the adults' lack of preference, shows that children do not respect the subject-last principle of ordering Tagalog full noun phrases. These results suggest that language-specific optional features like a subject-last principle take longer to be acquired.},
  language  = {en}
}
@article{HeckerSteckhanEybenetal.2022,
  author    = {Hecker, Pascal and Steckhan, Nico and Eyben, Florian and Schuller, Bj{\"o}rn Wolfgang and Arnrich, Bert},
  title     = {Voice Analysis for Neurological Disorder Recognition - A Systematic Review and Perspective on Emerging Trends},
  series = {Frontiers in Digital Health},
  journal   = {Frontiers in Digital Health},
  publisher = {Frontiers Media SA},
  address   = {Lausanne, Schweiz},
  issn      = {2673-253X},
  doi       = {10.3389/fdgth.2022.842301},
  pages     = {16},
  year      = {2022},
  abstract  = {Quantifying neurological disorders from voice is a rapidly growing field of research and holds promise for unobtrusive and large-scale disorder monitoring. The data recording setup and data analysis pipelines are both crucial aspects to effectively obtain relevant information from participants. Therefore, we performed a systematic review to provide a high-level overview of practices across various neurological disorders and highlight emerging trends. PRISMA-based literature searches were conducted through PubMed, Web of Science, and IEEE Xplore to identify publications in which original (i.e., newly recorded) datasets were collected. Disorders of interest were psychiatric as well as neurodegenerative disorders, such as bipolar disorder, depression, and stress, as well as amyotrophic lateral sclerosis amyotrophic lateral sclerosis, Alzheimer's, and Parkinson's disease, and speech impairments (aphasia, dysarthria, and dysphonia). Of the 43 retrieved studies, Parkinson's disease is represented most prominently with 19 discovered datasets. Free speech and read speech tasks are most commonly used across disorders. Besides popular feature extraction toolkits, many studies utilise custom-built feature sets. Correlations of acoustic features with psychiatric and neurodegenerative disorders are presented. In terms of analysis, statistical analysis for significance of individual features is commonly used, as well as predictive modeling approaches, especially with support vector machines and a small number of artificial neural networks. An emerging trend and recommendation for future studies is to collect data in everyday life to facilitate longitudinal data collection and to capture the behavior of participants more naturally. Another emerging trend is to record additional modalities to voice, which can potentially increase analytical performance.},
  language  = {en}
}