Attention: This page shows a discontinued module offer.
Discontinued
10 Credit points
For information on the duration of the modul, refer to the courses of study in which the module is used.
Non-official translation of the module descriptions. Only the German version is legally binding.
In this lecture students will learn basic methods of automatic speech recognition (ASR) as they are used both in research prototypes as well as in off-the-shelf ASR systems. After this lecture students should be able to critically examine the performance of ASR systems and to train and evaluate an ASR system such as Esmeralda or HTK.
The lecture Speech Recognition focuses on methods to automatically derive an orthographic representation from spoken speech as used in automatic dictating machines or for the speech-based control of technical systems. We will start-off with an overview of the human articulatory system in order to better understand the components of the speech signal in terms of the source-filter model as well as phonetic phenomena such as coarticulation or reduction. This forms the basis for the signal processing methods where the different components of source and filter are decomposed. One important part of the lecture are Hidden-Markov-Models (HMMs) which still represent the paradigm of state-of-the-art ASR approaches. The mathematical basis of this statistical modeling approach will be discussed and algorithms for training and decoding of HMMs given a speech signal and an annotation for training will be presented in detail. Several variants of ASR systems will be discussed.
The lecture "Application oriented speech processing" will present concrete implementations of the algorithms discussed in the lecture "Speech Recognition". Within the exercises advanced ASR techniques will be derived at a theoretical level and will be implemented and evaluated in group projects.
Alternatively to this lecture selected topics of ASR will be discusse in a seminar. In the seminar participants are expected to prepare and give a presentation on a specific topic as well as provide a written summary.
Recommended Competences: Competences as they can be achieved with the Module 39-Inf-MK Pattern Recognition
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Notes on course selection:
You must attend either the lecture and tutorial Application oriented speech processing or the seminar speech processing.
Ungraded / Graded Module Examination:
The (partial) examination of the module can be performed as "ungraded" in some study programs at the students choice. Before the examination a respective determination must be carried out, a later modification (graded - ungraded) is impossible. If the "ungraded" option is chosen, it is not possible to include this module in a study program where this module is deemed to enter the calculation of the overall grade.
Module structure: 1 SL, 0-1 bPr, 0-1 uPr 1
Alternatively to lecture and exercises a seminar language processing can be chosen.
Alternatively to lecture and exercises a seminar language processing can be chosen.
| Allocated examiner | Workload | LP2 |
|---|---|---|
|
Teaching staff of the course
Anwendungsorientierte Sprachverarbeitung
(exercise)
Oral presentation (15-25 min.) |
see above |
see above
|
|
Teaching staff of the course
Sprachverarbeitung
(seminar)
Oral presentation (15-25 min.) |
see above |
see above
|
In some degree programmes of the Faculty of Technology, the module examination can also be "ungraded" at the student's discretion (see explanations of the module elements and the respective subject-specific regulations). If the ungraded option is selected, it is not possible to use this module for a degree programme in which this module is taken into account in the overall grade calculation.
See below for explanations of this examination (graded examination option).
Oral examination (15-25 min.) about the contents of lecture and exercises.
Bei diesem Modul handelt es sich um ein eingestelltes Angebot. Ein entsprechendes Angebot, um dieses Modul abzuschließen, wurde bis maximal Sommersemester 2019 vorgehalten.
Bisheriger Angebotsturnus war jedes Wintersemester.
| Degree programme | Profile | Recommended start 3 | Duration | Mandatory option 4 |
|---|---|---|---|---|
| Data Science / Master of Science [FsB vom 06.04.2018 mit Änderungen vom 01.07.2019, 02.03.2020, 21.03.2023 und 10.12.2024] | Variante 1 | 3. | two semesters | Compulsory optional subject |
| Data Science / Master of Science [FsB vom 06.04.2018 mit Änderungen vom 01.07.2019, 02.03.2020, 21.03.2023 und 10.12.2024] | Variante 2 | 3. | two semesters | Compulsory optional subject |
| Intelligent Systems / Master of Science [FsB vom 27.07.2018 mit Änderung vom 04.06.2020] | 1. | two semesters | Compulsory optional subject | |
| Intelligent Systems / Master of Science [FsB vom 17.12.2012 mit Änderungen vom 15.04.2013, 01.04.2014, 15.10.2014, 02.03.2015 und Berichtigung vom 17.11.2014] | 1. | two semesters | Compulsory optional subject | |
| Informatics for the Natural Sciences / Master of Science [FsB vom 30.09.2016 mit Berichtigung vom 10.01.2017 und Änderungen vom 15.09.2017, 02.05.2018, 04.06.2020 und 31.03.2023] | 1. o. 3. | two semesters | Compulsory optional subject | |
| Informatics for the Natural Sciences / Master of Science [FsB vom 17.12.2012 mit Änderungen vom 15.04.2013, 01.04.2014, 15.10.2014, 02.03.2015, 01.12.2015 und Berichtigungen vom 01.04.2014, 17.11.2014 und 12.07.2017] | 1. o. 3. | two semesters | Compulsory optional subject |
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