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Transkriptionswerkzeuge sind spezialisierte Softwaretools für die Transkription und Annotation von Audio- oder Videoaufzeichnungen gesprochener Sprache. Dieses Kapitel erklärt einleitend, worin der zusätzliche Nutzen solcher Werkzeuge gegenüber einfacher Textverarbeitungssoftware liegt, und gibt dann einen Überblick über grundlegende Prinzipien und einige weitverbreitete Tools dieser Art. Am Beispiel der Editoren FOLKER und OrthoNormal wird schließlich der praktische Einsatz zweier Werkzeuge in den Arbeitsabläufen eines Korpusprojekts illustriert.
The newest generation of speech technology caused a huge increase of audio-visual data nowadays being enhanced with orthographic transcripts such as in automatic subtitling in online platforms. Research data centers and archives contain a range of new and historical data, which are currently only partially transcribed and therefore only partially accessible for systematic querying. Automatic Speech Recognition (ASR) is one option of making that data accessible. This paper tests the usability of a state-of-the-art ASR-System on a historical (from the 1960s), but regionally balanced corpus of spoken German, and a relatively new corpus (from 2012) recorded in a narrow area. We observed a regional bias of the ASR-System with higher recognition scores for the north of Germany vs. lower scores for the south. A detailed analysis of the narrow region data revealed – despite relatively high ASR-confidence – some specific word errors due to a lack of regional adaptation. These findings need to be considered in decisions on further data processing and the curation of corpora, e.g. correcting transcripts or transcribing from scratch. Such geography-dependent analyses can also have the potential for ASR-development to make targeted data selection for training/adaptation and to increase the sensitivity towards varieties of pluricentric languages.
The newest generation of speech technology caused a huge increase of audio-visual data nowadays being enhanced with orthographic transcripts such as in automatic subtitling in online platforms. Research data centers and archives contain a range of new and historical data, which are currently only partially transcribed and therefore only partially accessible for systematic querying. Automatic Speech Recognition (ASR) is one option of making that data accessible. This paper tests the usability of a state-of-the-art ASR-System on a historical (from the 1960s), but regionally balanced corpus of spoken German, and a relatively new corpus (from 2012) recorded in a narrow area. We observed a regional bias of the ASR-System with higher recognition scores for the north of Germany vs. lower scores for the south. A detailed analysis of the narrow region data revealed – despite relatively high ASR-confidence – some specific word errors due to a lack of regional adaptation. These findings need to be considered in decisions on further data processing and the curation of corpora, e.g. correcting transcripts or transcribing from scratch. Such geography-dependent analyses can also have the potential for ASR-development to make targeted data selection for training/adaptation and to increase the sensitivity towards varieties of pluricentric languages.
Korpora gesprochener Sprache
(2022)
Korpora gesprochener Sprache bestehen aus Audio- oder Videoaufnahmen sprachlicher Produktionen, die über eine Transkription einer linguistischen Analyse zugänglich gemacht werden. Sie kommen zur Untersuchung unterschiedlichster sprachwissenschaftlicher Fragestellungen unter anderem in der Gesprächsforschung, der Dialektologie und der Phonetik zum Einsatz. Dieser Beitrag diskutiert die wichtigsten Eigenschaften von Korpora gesprochener Sprache und stellt einige Vertreter der verschiedenen Kategorien vor.
This presentation introduces a new collaborative project: the International Comparable Corpus (ICC) (https://korpus.cz/icc), to be compiled from European national, standard(ised) languages, using the protocols for text categories and their quantities of texts in the International Corpus of English (ICE).
This paper presents experiments on sentence boundary detection in transcripts of spoken dialogues. Segmenting spoken language into sentence-like units is a challenging task, due to disfluencies, ungrammatical or fragmented structures and the lack of punctuation. In addition, one of the main bottlenecks for many NLP applications for spoken language is the small size of the training data, as the transcription and annotation of spoken language is by far more time-consuming and labour-intensive than processing written language. We therefore investigate the benefits of data expansion and transfer learning and test different ML architectures for this task. Our results show that data expansion is not straightforward and even data from the same domain does not always improve results. They also highlight the importance of modelling, i.e. of finding the best architecture and data representation for the task at hand. For the detection of boundaries in spoken language transcripts, we achieve a substantial improvement when framing the boundary detection problem as a sentence pair classification task, as compared to a sequence tagging approach.
Gesprochene Lernerkorpora: Methodisch-technische Aspekte der Erhebung, Erschließung und Nutzung
(2022)
This article provides an overview of methodological and technical issues that arise in the collection, indexing and use of spoken learner corpora, i. e. corpora containing spoken utterances of learners of a target language. After an introductory discussion of the most important special features of this type of corpus that distinguish it from written language learner corpora and spoken corpora with L1 speakers, we will go into more detail on questions of corpus design. The main part of the paper is then an overview of the methodological and technical procedures of the individual steps of collecting, indexing, providing and using spoken learner corpora. The main aim of this overview is to highlight practices that can be considered best practices according to the current state of research. Finally, we outline the challenges that still exist for this type of corpus.
Older adults are often exposed to elderspeak, a specialized speech register linked with negative outcomes. However, previous research has mainly been conducted in nursing homes without considering multiple contextual conditions. Based on a novel contextually-driven framework, we examined elderspeak in an acute general versus geriatric German hospital setting. Individuallevel information such as cognitive impairment (CI) and audio-recorded data from care interactions between 105 older patients (M = 83.2 years; 49% with severe CI) and 34 registered nurses (M = 38.9 years) were assessed. Psycholinguistic analyses were based on manual coding (k = .85 to k = .97) and computer-assisted procedures. First, diminutives (61%), collective pronouns (70%), and tag questions (97%) were detected. Second, patients’ functional impairment emerged as an important factor for elderspeak. Our study suggests that functional impairment may be a more salient trigger of stereotype activation than CI and that elderspeak deserves more attention in acute hospital settings.