Sammelbandbeitrag - Artikel | | Not reviewed Open Access

Reproducing an experiment in automatic disfluency detection

Autor(en)
Grimm, Frank ; Schlangen, David ; Hough, Julian ; Cimiano, Philipp 0000-0002-4771-441X
ISBN-13
9783943363067
URN
urn:nbn:de:0070-pub-29537945
URL
https://pub.uni-bielefeld.de/record/2953794

Weitere Details

Sprache
englisch
Darstellungsform
Text
Seitenbereich
97 - 112
Schlagwörter
HMM , Keras , LSTM , Linguistics , Machine learning , NLTK , Python , RNN , Speech Recognition , Theano
Link zum Volltext (intern)
https://pub.uni-bielefeld.de/record/2953794
DDC
410

In this chapter, we describe an effort to reproduce the main results of the published paper “Joint, Incremental Disfluency Detection and Utterance Segmentation from Speech” [1], published as part of the proceedings of the “European Chapter of the Association for Computational Linguistics” (EACL) in 2017. The paper focuses on the task of disfluency detection and utterance segmentation and proposes a simple deep learning system that processes dialogue transcriptions and Automatic Speech Recognition (ASR) output. For this purpose, the Dialogue Systems Group (DSG) at Bielefeld University developed a library that relies on a data model for live ASR data that combines timing and textual information. It utilizes a refined text corpus of open data to demonstrate the feasibility of the system for simultaneously detecting disfluencies and segmenting the individual utterances for use in conversational systems and similar speech related tasks. The code and data for this reproducibility experiment are available at https://gitlab.ub.uni-bielefeld.de/conquaire/deep_disfluency.

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