Natural Language and Speech Processing
BASIC DATA
course listing
A - main register
course code
ITS8040
course title in Estonian
Loomuliku keele ja kõne töötlus
course title in English
Natural Language and Speech Processing
course volume CP
-
ECTS credits
6.00
to be declared
yes
fully online course
not
assessment form
Examination
teaching semester
spring
language of instruction
Estonian
English
Study programmes that contain the course
Structural units teaching the course
IT - Department of Software Science
Timetable link
Version:
VERSION SPECIFIC DATA
course aims in Estonian
Osata rakendada ja kombineerida algoritme kirjaliku ja suulise keele andmete automatiseeritud töötluseks masinõppe meetodite abil
course aims in English
To gain skills to apply and combine algorithms for automated processing of natural language in written and vocal form.
learning outcomes in the course in Est.
Aine läbinud üliõpilane
• omab ülevaadet erinevatest loomuliku keele ja kõne töötluse valdkondadest;
• tunneb tähtsamaid loomuliku keele töötluse põhimõtteid ja ideid;
• oskab valida loomuliku keele andmete töötluseks sobiva algoritme ja neid kombineerida ning rakendada;
• tunneb kõnetuvastuse põhimõtteid.
• omab ülevaadet erinevatest loomuliku keele ja kõne töötluse valdkondadest;
• tunneb tähtsamaid loomuliku keele töötluse põhimõtteid ja ideid;
• oskab valida loomuliku keele andmete töötluseks sobiva algoritme ja neid kombineerida ning rakendada;
• tunneb kõnetuvastuse põhimõtteid.
learning outcomes in the course in Eng.
The student who has passed the course:
• has an overview of different approaches to natural language and speech processing;
• knows the most important principles and ideas of natural language processing;
• Is able to select, combine and apply appropriate algorithms for processing natural language data.
• has an overview of different approaches to natural language and speech processing;
• knows the most important principles and ideas of natural language processing;
• Is able to select, combine and apply appropriate algorithms for processing natural language data.
brief description of the course in Estonian
Kaasaegse korpuse-põhise loomuliku keele töötluse põhimõisted ja rakendusvaldkonnad.
Loomuliku keele andmete tehniline eeltöötlus.
Teksti transformeerimine lõplike muundurite abil.
Masinõpe ja selle rakendamine loomuliku keele töötluses: sõnade ja dokumentide klassifitseerimine, sõna-vektor mudelid, rekurrentsed närvivõrgud.
Kõnetuvastus: tunnuste eraldamine kõnesignaalist, akustilised mudelid, keelemudelid, sügavate närvivõrkude kasutamine kõnetuvastuses.
Loomuliku keele andmete tehniline eeltöötlus.
Teksti transformeerimine lõplike muundurite abil.
Masinõpe ja selle rakendamine loomuliku keele töötluses: sõnade ja dokumentide klassifitseerimine, sõna-vektor mudelid, rekurrentsed närvivõrgud.
Kõnetuvastus: tunnuste eraldamine kõnesignaalist, akustilised mudelid, keelemudelid, sügavate närvivõrkude kasutamine kõnetuvastuses.
brief description of the course in English
Contemporary corpus based natural language processing concepts and application domains.
Technical preprocessing of natural language data.
Text transformation using finite transducers.
Application of machine learning in the context of natural language processing: classification of words and documents, word vector models, recurrent neural networks.
Speech processing: extraction of features from speech signal, acoustic models, language models, application of deep neural networks in speech processing.
Technical preprocessing of natural language data.
Text transformation using finite transducers.
Application of machine learning in the context of natural language processing: classification of words and documents, word vector models, recurrent neural networks.
Speech processing: extraction of features from speech signal, acoustic models, language models, application of deep neural networks in speech processing.
type of assessment in Estonian
...
type of assessment in English
...
independent study in Estonian
-
independent study in English
-
study literature
Daniel Jurafsky, James H. Martin. “Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition”, Prentice Hall, 2008.
Ian Goodfellow, Yoshua Bengio, Aaron Courville, “Deep Learning”, MIT Press, 2016
Ian Goodfellow, Yoshua Bengio, Aaron Courville, “Deep Learning”, MIT Press, 2016
study forms and load
daytime study: weekly hours
4.0
session-based study work load (in a semester):
lectures
2.0
lectures
-
practices
0.0
practices
-
exercises
2.0
exercises
-
lecturer in charge
-
LECTURER SYLLABUS INFO
semester of studies
teaching lecturer / unit
language of instruction
Extended syllabus
2025/2026 spring
Tanel Alumäe, IT - Department of Software Science
English