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  AlignTool: The automatic temporal alignment of spoken utterances in German, Dutch, and British English for psycholinguistic purposes

Schillingmann, L., Ernst, J., Keite, V., Wrede, B., Meyer, A. S., & Belke, E. (2018). AlignTool: The automatic temporal alignment of spoken utterances in German, Dutch, and British English for psycholinguistic purposes. Behavior Research Methods, 50(2), 466-489. doi:10.3758/s13428-017-1002-7.

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Schillingmann_etal_2018.pdf (Verlagsversion), 3MB
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 Urheber:
Schillingmann, Lars1, Autor
Ernst, Jessica2, Autor
Keite, Verena2, Autor
Wrede, Britta1, Autor
Meyer, Antje S.3, 4, Autor           
Belke, Eva2, Autor
Affiliations:
1Technische FakultätUniversität Bielefeld, Bielefeld, Germany, ou_persistent22              
2Sprachwissenschaftliches Institut, Ruhr-Universität Bochum, Bochum, Germany, ou_persistent22              
3Psychology of Language Department, MPI for Psycholinguistics, Max Planck Society, ou_792545              
4Radboud University Nijmegen, ou_persistent22              

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Schlagwörter: Language production ; Time course ; Voice-key ; Automatic alignment
 Zusammenfassung: In language production research, the latency with which speakers produce a spoken response to a stimulus and the onset and offset times of words in longer utterances are key dependent variables. Measuring these variables automatically often yields partially incorrect results. However, exact measurements through the visual inspection of the recordings are extremely time-consuming. We present AlignTool, an open-source alignment tool that establishes preliminarily the onset and offset times of words and phonemes in spoken utterances using Praat, and subsequently performs a forced alignment of the spoken utterances and their orthographic transcriptions in the automatic speech recognition system MAUS. AlignTool creates a Praat TextGrid file for inspection and manual correction by the user, if necessary. We evaluated AlignTool’s performance with recordings of single-word and four-word utterances as well as semi-spontaneous speech. AlignTool performs well with audio signals with an excellent signal-to-noise ratio, requiring virtually no corrections. For audio signals of lesser quality, AlignTool still is highly functional but its results may require more frequent manual corrections. We also found that audio recordings including long silent intervals tended to pose greater difficulties for AlignTool than recordings filled with speech, which AlignTool analyzed well overall. We expect that by semi-automatizing the temporal analysis of complex utterances, AlignTool will open new avenues in language production research.

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Sprache(n): eng - English
 Datum: 20182018
 Publikationsstatus: Erschienen
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 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.3758/s13428-017-1002-7
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Titel: Behavior Research Methods
Genre der Quelle: Zeitschrift
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Ort, Verlag, Ausgabe: Austin, TX : Psychonomic Society
Seiten: - Band / Heft: 50 (2) Artikelnummer: - Start- / Endseite: 466 - 489 Identifikator: ISSN: 1554-3528
CoNE: https://pure.mpg.de/cone/journals/resource/1554-3528