Speaker adaptation for word prominence detection:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Abschlussarbeit Buch |
| Sprache: | English |
| Veröffentlicht: |
Aachen
Shaker Verlag
2018
|
| Ausgabe: | 1. Auflage |
| Schriftenreihe: | Berichte aus der Informationstechnik
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XI, 122 Seiten Illustrationen, Diagramme 21 cm x 14.8 cm, 206 g |
| ISBN: | 9783844059212 3844059210 |
Internformat
MARC
| LEADER | 00000nam a22000008c 4500 | ||
|---|---|---|---|
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| 003 | DE-604 | ||
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| 100 | 1 | |a Schnall, Andrea |d 1986- |e Verfasser |0 (DE-588)1159790655 |4 aut | |
| 245 | 1 | 0 | |a Speaker adaptation for word prominence detection |c Andrea Schnall |
| 250 | |a 1. Auflage | ||
| 264 | 1 | |a Aachen |b Shaker Verlag |c 2018 | |
| 300 | |a XI, 122 Seiten |b Illustrationen, Diagramme |c 21 cm x 14.8 cm, 206 g | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Berichte aus der Informationstechnik | |
| 502 | |b Dissertation |c Technische Universität Darmstadt |d 2018 | ||
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| 650 | 0 | 7 | |a Berühmte Persönlichkeit |0 (DE-588)4191412-0 |2 gnd |9 rswk-swf |
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| 650 | 0 | 7 | |a Automatische Klassifikation |0 (DE-588)4120957-6 |2 gnd |9 rswk-swf |
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| 650 | 0 | 7 | |a Automatische Sprechererkennung |0 (DE-588)4143704-4 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Kontextbezogenes System |0 (DE-588)4739720-2 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Sprecheradaption |0 (DE-588)4327834-6 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Support-Vektor-Maschine |0 (DE-588)4505517-8 |2 gnd |9 rswk-swf |
| 653 | |a Prosody | ||
| 653 | |a SVM | ||
| 653 | |a Speaker Adaptation | ||
| 653 | |a Word Prominence Detection | ||
| 653 | |a fMLLR | ||
| 655 | 7 | |0 (DE-588)4113937-9 |a Hochschulschrift |2 gnd-content | |
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Datensatz im Suchindex
| _version_ | 1804178607274196992 |
|---|---|
| adam_text | C O N TEN TS
LIST O F SYM B OLS V I
A B STRACT V III
1 IN TROD U CTION 1
1.1 CONTRIBUTION
............................................................................
5
1.2 OUTLINE OF THE
DISSERTATION....................................................... 6
2 W ORD PROM IN EN CE D ETE CTIO N IN SPOKEN DIALOG SY STEM S 8
2.1 P RO S O D Y
.....................................................................................
8
2.2 WORD
PROMINENCE......................................................................
9
2.3 WORD PROMINENCE D E TE C TIO N
.................................................... 10
2.3.1 PROSODY IN SPOKEN DIALOG SYSTEM S
..............................
10
2.3.2 FEATURE T Y P E S
................................................................ 12
2.3.3 SYLLABLE OR WORD LEVEL
................................................. 12
2.4 SPEAKER ADAPTATION FOR WORD PROMINENCE D E TE C TIO N
............
13
2.5 S U M M A RY
.............................................
13
3 S Y STEM ARCH ITECTU RE AND COM P ON EN TS 15
3.1 PROPOSED SYSTEM A RC H ITE C TU RE
................................................. 15
3.2 DATA S E T
.....................................................................................
17
3.2.1 SETTING OF THE USED DATA S E T
........................................ 18
3.2.2 A
NNOTATIONS...................................................................
20
3.2.3 SYLLABLE OR WORD LEVEL
................................................. 20
3.3 F E A TU RE
S.....................................................................................
21
3.3.1 AUDIO F E A TU R E S
............................................................. 21
3.3.2 VISUAL F E A TU RE S
............................................................. 23
3.3.3 F U N CTIO N
ALS...................................................................
24
3.3.4 CONTEXT F E A TU R E S
.......................................................... 24
3.3.5 FEATURE NORM
ALIZATION................................................. 25
3.3.6 PRINCIPAL COMPONENT ANALYSIS, P C A
........................
26
3.4 CLASSIFICATION METHODS FOR WORD PROMINENCE DETECTION . . . 27
3.4.1 SUPPORT VECTOR MACHINES, SV M
...................................
28
3.4.2 GAUSSIAN MIXTURE MODELS, G M M ............................... 32
3.4.3 CONDITIONAL RANDOM FIELDS, CRF .......................... 33
3.4.4 DEEP NEURAL NETWORKS, D N N .....................................
35
3.4.5 CLASSIFICATION PERFORMANCE M EASUREM ENT
..................
35
3.5 EXPERIMENTAL S E TU P
...................................................................
37
3.6 S U M M A RY
..................................................................................
38
4 IN TEGRATIN G CO N TEX T INFORM ATION 39
4.1 S TATE-OF-THE-ART
.........................................................................
40
4.2 INCLUDING SEQUENCE IN FO RM ATIO N
.............................................. 41
4.2.1 M ETHODS
.........................................................................
41
4.2.2 CONTEXT
REGIONS............................................................. 42
4.3 FEATURE C OM
BINATIONS.............................................................
44
4.4 EXPERIM ENTS
...............................................................................
45
4.4.1 DIFFERENT FEATURE COMBINATIONS AND CLASSIFIERS .... 45
4.4.2 DIFFERENT CONTEXT REGIONS
..............................
46
4.4.3 PARTIAL FEATURE C O N TE X
T................................................. 48
4.4.4 CONTEXT L A B E L
................................................................ 51
4.4.5 JAPANESE DATA S E T
.......................................................... 53
4.5 DISCUSSION
..................................................................................
53
4.6 CONCLUSION & S U M M A RY
.......................................................... 54
5 S P EAKER-D EP EN D EN T VS. SP EAK ER-IN D EP EN D EN T TRAINING 56
5.1 STATE-OF-THE A R T
.........................................................................
56
5.2 EXPERIMENTS O VERVIEW
............................................................. 59
5.3 FEATURE PERFORM
ANCE................................................................ 59
5.3.1 SPEAKER-DEPENDENT TR A IN IN G
........................................ 60
5.3.2 SPEAKER-INDEPENDENT TRAINING, MODEL TRAINED ON ONE
S P E A K E R
.........................................................................
63
5.3.3 SPEAKER-INDEPENDENT TRAINING, TRAINED ON A SET OF
SPEAKERS
.........................................................................
65
5.3.4 SPEAKER-INDEPENDENT TRAINING, FEATURE CHOICE .... 67
5.4 DIFFERENT CLASSIFIERS FOR SPEAKER-INDEPENDENT TRAINING .... 69
5.5 CONCLUSION & S U M M A RY
.......................................................... 74
6 S V M BASED SPEAKER AD AP TATION 76
6.1 SVM ADAPTATION - STATE OF THE A R T
........................................ 80
6.2 FEATURE-SPACE M L L R
................................................................
81
6.2.1 ROW-BY-ROW ITERATION
..................................................
84
6.3 ADAPTATION TO SVM DECISION BOUNDARY
...................................
85
6.3.1 FEATURE-SPACE SVM ADAPTATION, FS V M A
..................
85
6.3.2 MODEL-BASED SVM ADAPTATION, S V M A
.......................
86
6.3.3 GAUSSIAN REGULARIZED FEATURE-SPACE SVM ADAPTA
TION, GRFSVMA 87
6.3.4 SPARSENESS REGULARIZATION
........................................... 88
6.3.5 METHOD OVERVIEW
.......................................................... 89
6.4 VALIDATION ON ARTIFICIAL DATA E X A M P LE
..................................... 89
6.5 EXPERIMENTAL S E TU P
................................................................... 91
6.5.1 P G A
...............................................................................
92
6.6 EXPERIM
ENTS...............................................................................
93
6.6.1 DIFFERENT NUMBER OF GAUSSIANS
.................................
94
6.6.2 DIFFERENT WEIGHTING OF PARAM ETERS
..............................
97
6.6.3 DIFFERENT SPEAKER TY P E S
...................................................
100
6.7 DISCUSSION A D A P TA TIO N
................................................................102
6.8 CONCLUSION & S U M M A RY
.............................................................104
7 C ON CLU SION 106
7.1 S U M M A RY
.....................................................................................
106
7.2 FUTURE W O RK
..................................................................................
108
B IBLIOGRAP HY 110
IN DEX
121
|
| any_adam_object | 1 |
| author | Schnall, Andrea 1986- |
| author_GND | (DE-588)1159790655 |
| author_facet | Schnall, Andrea 1986- |
| author_role | aut |
| author_sort | Schnall, Andrea 1986- |
| author_variant | a s as |
| building | Verbundindex |
| bvnumber | BV045001841 |
| classification_rvk | ZN 6070 ES 945 |
| ctrlnum | (OCoLC)1039098081 (DE-599)DNB1155609492 |
| discipline | Sprachwissenschaft Elektrotechnik / Elektronik / Nachrichtentechnik Literaturwissenschaft |
| edition | 1. Auflage |
| format | Thesis Book |
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| genre_facet | Hochschulschrift |
| id | DE-604.BV045001841 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T08:06:39Z |
| institution | BVB |
| isbn | 9783844059212 3844059210 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030393966 |
| oclc_num | 1039098081 |
| open_access_boolean | |
| owner | DE-83 |
| owner_facet | DE-83 |
| physical | XI, 122 Seiten Illustrationen, Diagramme 21 cm x 14.8 cm, 206 g |
| publishDate | 2018 |
| publishDateSearch | 2018 |
| publishDateSort | 2018 |
| publisher | Shaker Verlag |
| record_format | marc |
| series2 | Berichte aus der Informationstechnik |
| spelling | Schnall, Andrea 1986- Verfasser (DE-588)1159790655 aut Speaker adaptation for word prominence detection Andrea Schnall 1. Auflage Aachen Shaker Verlag 2018 XI, 122 Seiten Illustrationen, Diagramme 21 cm x 14.8 cm, 206 g txt rdacontent n rdamedia nc rdacarrier Berichte aus der Informationstechnik Dissertation Technische Universität Darmstadt 2018 Sprachdialogsystem (DE-588)1028331231 gnd rswk-swf Berühmte Persönlichkeit (DE-588)4191412-0 gnd rswk-swf Radiale Basisfunktion (DE-588)4380647-8 gnd rswk-swf Automatische Klassifikation (DE-588)4120957-6 gnd rswk-swf Merkmalsextraktion (DE-588)4314440-8 gnd rswk-swf Automatische Sprechererkennung (DE-588)4143704-4 gnd rswk-swf Kontextbezogenes System (DE-588)4739720-2 gnd rswk-swf Sprecheradaption (DE-588)4327834-6 gnd rswk-swf Support-Vektor-Maschine (DE-588)4505517-8 gnd rswk-swf Prosody SVM Speaker Adaptation Word Prominence Detection fMLLR (DE-588)4113937-9 Hochschulschrift gnd-content Sprachdialogsystem (DE-588)1028331231 s Automatische Sprechererkennung (DE-588)4143704-4 s Sprecheradaption (DE-588)4327834-6 s Support-Vektor-Maschine (DE-588)4505517-8 s Radiale Basisfunktion (DE-588)4380647-8 s Berühmte Persönlichkeit (DE-588)4191412-0 s Kontextbezogenes System (DE-588)4739720-2 s Automatische Klassifikation (DE-588)4120957-6 s Merkmalsextraktion (DE-588)4314440-8 s DE-604 DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030393966&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Schnall, Andrea 1986- Speaker adaptation for word prominence detection Sprachdialogsystem (DE-588)1028331231 gnd Berühmte Persönlichkeit (DE-588)4191412-0 gnd Radiale Basisfunktion (DE-588)4380647-8 gnd Automatische Klassifikation (DE-588)4120957-6 gnd Merkmalsextraktion (DE-588)4314440-8 gnd Automatische Sprechererkennung (DE-588)4143704-4 gnd Kontextbezogenes System (DE-588)4739720-2 gnd Sprecheradaption (DE-588)4327834-6 gnd Support-Vektor-Maschine (DE-588)4505517-8 gnd |
| subject_GND | (DE-588)1028331231 (DE-588)4191412-0 (DE-588)4380647-8 (DE-588)4120957-6 (DE-588)4314440-8 (DE-588)4143704-4 (DE-588)4739720-2 (DE-588)4327834-6 (DE-588)4505517-8 (DE-588)4113937-9 |
| title | Speaker adaptation for word prominence detection |
| title_auth | Speaker adaptation for word prominence detection |
| title_exact_search | Speaker adaptation for word prominence detection |
| title_full | Speaker adaptation for word prominence detection Andrea Schnall |
| title_fullStr | Speaker adaptation for word prominence detection Andrea Schnall |
| title_full_unstemmed | Speaker adaptation for word prominence detection Andrea Schnall |
| title_short | Speaker adaptation for word prominence detection |
| title_sort | speaker adaptation for word prominence detection |
| topic | Sprachdialogsystem (DE-588)1028331231 gnd Berühmte Persönlichkeit (DE-588)4191412-0 gnd Radiale Basisfunktion (DE-588)4380647-8 gnd Automatische Klassifikation (DE-588)4120957-6 gnd Merkmalsextraktion (DE-588)4314440-8 gnd Automatische Sprechererkennung (DE-588)4143704-4 gnd Kontextbezogenes System (DE-588)4739720-2 gnd Sprecheradaption (DE-588)4327834-6 gnd Support-Vektor-Maschine (DE-588)4505517-8 gnd |
| topic_facet | Sprachdialogsystem Berühmte Persönlichkeit Radiale Basisfunktion Automatische Klassifikation Merkmalsextraktion Automatische Sprechererkennung Kontextbezogenes System Sprecheradaption Support-Vektor-Maschine Hochschulschrift |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030393966&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT schnallandrea speakeradaptationforwordprominencedetection |