Automatisk Identifiering av Inandningspauser i Spontant Tal - ett HMM/ANN-hybridsystem i Matlab
(2007)General Linguistics
- Abstract
- This thesis presents a system which has been implemented to satisfy a need in the
research on how speech planning interacts with syntactic and prosodic structure in
spontaneous speech. The long-term purpose of the research is to provide models for
automatic parsing of spontaneous speech and for psycholinguistical modelling of speech
production. Identification of inhalation pauses is an important step in the development
of automatic methods for spontaneous speech parsing.
Identification of inhalation pauses is considered to be a keyword-spotting speech
recognition problem. Hybrid HMM(Hidden Markov Models)/ANN(Artificial Neural
Networks) approach is applied to this problem. Method gets 90,8% in Recall, 66,4% in
Precision and 76,7%... (More) - This thesis presents a system which has been implemented to satisfy a need in the
research on how speech planning interacts with syntactic and prosodic structure in
spontaneous speech. The long-term purpose of the research is to provide models for
automatic parsing of spontaneous speech and for psycholinguistical modelling of speech
production. Identification of inhalation pauses is an important step in the development
of automatic methods for spontaneous speech parsing.
Identification of inhalation pauses is considered to be a keyword-spotting speech
recognition problem. Hybrid HMM(Hidden Markov Models)/ANN(Artificial Neural
Networks) approach is applied to this problem. Method gets 90,8% in Recall, 66,4% in
Precision and 76,7% in F-score. Use of a threshold value for duration increases the Fscore
to 82,5%, therefore duration is considered to be relevant in performance
optimization. Other proposed optimization parameters are better acoustic modelling,
identification of the units causing false identifications prior to inhalation pauses
identification and production of a more appropriate spontaneous speech corpus. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/1325350
- author
- Bystedt Pobelianskaia, Lena
- supervisor
- organization
- year
- 2007
- type
- H1 - Master's Degree (One Year)
- subject
- keywords
- inandningspauser, andning, nyckelordsidentifiering, 'HMM/ANN-hybridsystem', Matlab, taligenkänning, prosodi, talproduktion, akustik, fonetik, Linguistics, Allmän språkvetenskap/Lingvistik, Phonetics, phonology, Fonetik, fonologi, Technological sciences, Teknik
- language
- Swedish
- id
- 1325350
- date added to LUP
- 2007-11-12 00:00:00
- date last changed
- 2009-02-04 00:00:00
@misc{1325350,
abstract = {{This thesis presents a system which has been implemented to satisfy a need in the
research on how speech planning interacts with syntactic and prosodic structure in
spontaneous speech. The long-term purpose of the research is to provide models for
automatic parsing of spontaneous speech and for psycholinguistical modelling of speech
production. Identification of inhalation pauses is an important step in the development
of automatic methods for spontaneous speech parsing.
Identification of inhalation pauses is considered to be a keyword-spotting speech
recognition problem. Hybrid HMM(Hidden Markov Models)/ANN(Artificial Neural
Networks) approach is applied to this problem. Method gets 90,8% in Recall, 66,4% in
Precision and 76,7% in F-score. Use of a threshold value for duration increases the Fscore
to 82,5%, therefore duration is considered to be relevant in performance
optimization. Other proposed optimization parameters are better acoustic modelling,
identification of the units causing false identifications prior to inhalation pauses
identification and production of a more appropriate spontaneous speech corpus.}},
author = {{Bystedt Pobelianskaia, Lena}},
language = {{swe}},
note = {{Student Paper}},
title = {{Automatisk Identifiering av Inandningspauser i Spontant Tal - ett HMM/ANN-hybridsystem i Matlab}},
year = {{2007}},
}