392131 Introduction to Automatic Speech Recognition (V) (SoSe 2018)

Contents, comment

The goal of automatic speech recognition (ASR) is to map a spoken utterance, i.e. an acoustic speech signal, to an orthographic representation.
The lecture provides an introduction to signal processing methods based on acoustic and articulatory phonetic insights. The focus will be on Hidden-Markov-Models (HMMs) and associated algorithms. In detail, algorithms for parameter estimation and decoding will be presented, as well as for signal processing. The rich modeling space will be discussed with a range of standard variants.

Bibliography

  • Fink, G. A.: Mustererkennung mit Markov-Modellen, Leitfäden der Informatik, B. G. Teubner, Stuttgart - Leipzig - Wiesbaden, 2003.

* Schukat-Talamazzini, E.-G.: Automatische Spracherkennung, Vieweg, Wiesbaden, 1995.
* Huang, X., Acero, A., Hon, H-W.: Spoken Language Processing: A Guide to Theory, Algorithm, and System Development, Prentice Hall, Upper Saddle River, NJ, 2001.
* Clark & Yallop: Introduction to Phonetics and Phonology, 2007. url: tocs.ulb.tu-darmstadt.de/178080047.pdf

External comments page

http://www.zfl.uni-bielefeld.de/studium/module/techfak/modulhandbuch/#sprachsignalverarbeitung

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Module Course Requirements  
39-M-Inf-SSV Sprachsignalverarbeitung Spracherkennung Ungraded examination
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Student information

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Degree programme/academic programme Validity Variant Subdivision Status Semester LP  
Linguistik: Kommunikation, Kognition und Sprachtechnologie / Master (Enrollment until WiSe 19/20) 39-Inf-MaLinMSV   4  

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Last update basic details/teaching staff:
Thursday, March 19, 2020 
Last update times:
Friday, April 13, 2018 
Last update rooms:
Friday, April 13, 2018 
Type(s) / SWS (hours per week per semester)
V / 2
Language
This lecture is taught in english
Department
Faculty of Technology
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