Annotations and tools for an activity based Spoken Language Corpus

Allwood, Jens; Grönqvist, Leif

doi:10.3115/1118078.1118079

Cited by 14 publications

(9 citation statements)

References 10 publications

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“…This will also allow us to apply an annotation scheme that focuses on dialogue act types (e.g. Allwood et al, 2003) and that requires a more content-based analysis to see whether similar patterns of verbal and nonverbal expressions occur in combination with various types of dialogue acts.…”

Section: Discussionmentioning

confidence: 99%

Towards modeling expressed emotions in oral history interviews: Using verbal and nonverbal signals to track personal narratives

Truong

Westerhof

Lamers

et al. 2014

Lit Linguist Computing

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The article aims to model the verbal and prosodic features of emotional expression in interviews to investigate the potential for synergy between scholarly fields that have the narrative as object of study. Using a digital collection of oral history interviews that contains narrative aspects addressing war and violence in Croatia, we analyzed emotional expression through the words spoken, and through the pitch, vocal effort, and pause duration in the speech signal. The findings were correlated with the linear structure of interviews as well as question type. Our analysis indicates that the weight of emotion words for the overall expressed emotion is stronger in later interview parts as well as after open questions and meaning questions. Similar patterns were found for pitch and pause duration, but not for vocal effort. Although the verbal expression of emotions was somewhat correlated to pause duration, the hypothesized correlation between the verbal and nonverbal features was not confirmed. The research also shows that the various expressive layers in the interviews as well as the relations between them are a suited basis for computational modeling that may help track emotional personal narratives in interview collections. Additional research is needed to further develop the framework for the automated analysis of verbal and nonverbal cues to automatically generate annotations to be used for exploring spoken word collections.

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Section: Discussionmentioning

confidence: 99%

Towards modeling expressed emotions in oral history interviews: Using verbal and nonverbal signals to track personal narratives

Truong

Westerhof

Lamers

et al. 2014

Lit Linguist Computing

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“…The GSLC was created more opportunistically than the other corpora described here, which means without any prior corpus design. The main goal was to ensure the broadest possible range of different SEs [22]. The corpus consists of 1.42m words collected within 27 SE categories.…”

Section: Göteborg Spoken Language Corpus (Gslc)mentioning

confidence: 99%

“…These latter were either produced in the workplace, e.g. in a factory, travel agency, hotel, shop, at the doctor's, or in a task-oriented experimental setting (the complete list is available in [22]). The categorization in the GSLC is mostly ad hoc and low-level, no effort was made to establish any higher-level categories according to e.g.…”

Section: Göteborg Spoken Language Corpus (Gslc)mentioning

confidence: 99%

Relevant Criteria for Selection of Spoken Data: Theory Meets Practice

Kopřivová

Komrsková

Poukarová

et al. 2019

Journal of Linguistics/Jazykovedný Casopis

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The present paper seeks to review relevant criteria used in classifying speech events (SEs) from the perspective of spoken corpus design. The primary goal is to survey the landscape of possible types of spoken language, so as to assess in which directions the coverage of spoken Czech offered by Czech National Corpus corpora can be expanded in the future. We approach the problem from both theoretical and practical points of view, examining what the theoretical literature has to say as well as approaches implemented in practice by existing spoken corpora of various languages. We then synthesize the obtained information into a pragmatically motivated set of SE classification criteria which does not aspire to be universal or definitive but aims to serve as a useful guiding principle and conceptual framework for understanding and promoting SE diversity when collecting spoken data.

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“…The NNC spoken corpus has been designed to follow the template of the Göteborg Spoken Language Corpus (see Allwood et al, 2003). It consists of 260,000 words of data, collected from seventeen types of social activities, such as shopping, discussion, and so on.8 We made audio-video recordings of 116 occurrences of these activities in their natural context (thirtytwo hours), and then produced annotated orthographic transcriptions (in Devanagari).…”

Section: Spoken Textsmentioning

confidence: 99%

Construction and annotation of a corpus of contemporary Nepali

Yadava¹,

Hardie²,

Lohani³

et al. 2008

Corpora

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In this paper, we describe the construction of the 14-million-word Nepali National Corpus (NNC). This corpus includes both spoken and written data, the latter incorporating a Nepali match for FLOB and a broader collection of text. Additional resources within the NNC include parallel data (English–Nepali and Nepali–English) and a speech corpus. The NNC is encoded as Unicode text and marked up in CES-compatible XML. The whole corpus is also annotated with part-of-speech tags. We describe the process of devising a tagset and retraining tagger software for the Nepali language, for which there were no existing corpus resources. Finally, we explore some present and future applications of the corpus, including lexicography, NLP, and grammatical research.

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Annotations and tools for an activity based Spoken Language Corpus

Cited by 14 publications

References 10 publications

Towards modeling expressed emotions in oral history interviews: Using verbal and nonverbal signals to track personal narratives

Towards modeling expressed emotions in oral history interviews: Using verbal and nonverbal signals to track personal narratives

Relevant Criteria for Selection of Spoken Data: Theory Meets Practice

Construction and annotation of a corpus of contemporary Nepali

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