@phdthesis{Garcia2018,
  author    = {Garcia, Rowena},
  title     = {Thematic role assignment and word order preferences in the child language acquisition of Tagalog},
  address   = {Potsdam},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-421742},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xvii, 201},
  year      = {2018},
  abstract  = {A critical task in daily communications is identifying who did what to whom in an utterance, or assigning the thematic roles agent and patient in a sentence. This dissertation is concerned with Tagalog-speaking children's use of word order and morphosyntactic markers for thematic role assignment. It aims to explain children's difficulties in interpreting sentences with a non-canonical order of arguments (i.e., patient-before-agent) by testing the predictions of the following accounts: the frequency account (Demuth, 1989), the Competition model (MacWhinney \& Bates, 1989), and the incremental processing account (Trueswell \& Gleitman, 2004). Moreover, the experiments in this dissertation test the influence of a word order strategy in a language like Tagalog, where the thematic roles are always unambiguous in a sentence, due to its verb-initial order and its voice-marking system. In Tagalog's voice-marking system, the inflection on the verb indicates the thematic role of the noun marked by 'ang.' First, the possible basis for a word order strategy in Tagalog was established using a sentence completion experiment given to adults and 5- and 7-year-old children (Chapter 2) and a child-directed speech corpus analysis (Chapter 3). In general, adults and children showed an agent-before-patient preference, although adults' preference was also affected by sentence voice. Children's comprehension was then examined through a self-paced listening and picture verification task (Chapter 3) and an eye-tracking and picture selection task (Chapter 4), where word order (agent-initial or patient-initial) and voice (agent voice or patient voice) were manipulated. Offline (i.e., accuracy) and online (i.e., listening times, looks to the target) measures revealed that 5- and 7-year-old Tagalog-speaking children had a bias to interpret the first noun as the agent. Additionally, the use of word order and morphosyntactic markers was found to be modulated by voice. In the agent voice, children relied more on a word order strategy; while in the patient voice, they relied on the morphosyntactic markers. These results are only partially explained by the accounts being tested in this dissertation. Instead, the findings support computational accounts of incremental word prediction and learning such as Chang, Dell, \& Bock's (2006) model.},
  language  = {en}
}
@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}
}
@misc{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 = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Reihe der Digital Engineering Fakult{\"a}t},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Reihe der Digital Engineering Fakult{\"a}t},
  number    = {13},
  doi       = {10.25932/publishup-58101},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-581019},
  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}
}