Uyarlanir Yerel Bagli Katman Kullanan Dikkat Tabanli Derin Ag ile Sesli Komut Tanima

Yasemin Turkan; F. Boray Tek

doi:10.1109/SIU49456.2020.9302186

Uyarlanir Yerel Bagli Katman Kullanan Dikkat Tabanli Derin Ag ile Sesli Komut Tanima

Yasemin Turkan
, F. Boray Tek

Isik University

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

1 Atıf (Scopus)

Özet

Speech command recognition is an active research topic associated with the human-machine interface. Such problems can be successfully solved with attention-based deep networks. In this study, we improved one of the existing attentionbased deep network methods by using an adaptive locally connected (focused) layer. In the experiments we used Google and Kaggle datasets, which were also used in the reference. We observed that the recognition results can be improved significantly (2.6%) by the attention based deep network which uses adaptive locally connected layers.

Tercüme edilen katkı başlığı	Using Adaptive Locally Connected Layer in Attention Based Deep Neural Network for Speech Command Recognition
Orijinal dil	Türkçe
Ana bilgisayar yayını başlığı	2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)	9781728172064
DOI'lar	https://doi.org/10.1109/SIU49456.2020.9302186
Yayın durumu	Yayınlandı - 5 Eki 2020
Harici olarak yayınlandı	Evet
Etkinlik	28th Signal Processing and Communications Applications Conference, SIU 2020 - Gaziantep, Turkey Süre: 5 Eki 2020 → 7 Eki 2020

Yayın serisi

Adı	2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	28th Signal Processing and Communications Applications Conference, SIU 2020
Ülke/Bölge	Turkey
Şehir	Gaziantep
Periyot	5/10/20 → 7/10/20

Bibliyografik not

Publisher Copyright:
© 2020 IEEE.

Keywords

adaptive locally connected neuron
artificial neural networks
attention
speech command recognition

Belgeye Erişim

10.1109/SIU49456.2020.9302186

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Turkan, Y., & Tek, F. B. (2020). Uyarlanir Yerel Bagli Katman Kullanan Dikkat Tabanli Derin Ag ile Sesli Komut Tanima. In 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings Makale 9302186 (2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU49456.2020.9302186

Turkan, Yasemin ; Tek, F. Boray. / Uyarlanir Yerel Bagli Katman Kullanan Dikkat Tabanli Derin Ag ile Sesli Komut Tanima. 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. (2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings).

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title = "Uyarlanir Yerel Bagli Katman Kullanan Dikkat Tabanli Derin Ag ile Sesli Komut Tanima",

abstract = "Speech command recognition is an active research topic associated with the human-machine interface. Such problems can be successfully solved with attention-based deep networks. In this study, we improved one of the existing attentionbased deep network methods by using an adaptive locally connected (focused) layer. In the experiments we used Google and Kaggle datasets, which were also used in the reference. We observed that the recognition results can be improved significantly (2.6\%) by the attention based deep network which uses adaptive locally connected layers.",

keywords = "adaptive locally connected neuron, artificial neural networks, attention, speech command recognition",

author = "Yasemin Turkan and Tek, \{F. Boray\}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 28th Signal Processing and Communications Applications Conference, SIU 2020 ; Conference date: 05-10-2020 Through 07-10-2020",

year = "2020",

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doi = "10.1109/SIU49456.2020.9302186",

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publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Turkan, Y & Tek, FB 2020, Uyarlanir Yerel Bagli Katman Kullanan Dikkat Tabanli Derin Ag ile Sesli Komut Tanima. in 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings., 9302186, 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 28th Signal Processing and Communications Applications Conference, SIU 2020, Gaziantep, Turkey, 5/10/20. https://doi.org/10.1109/SIU49456.2020.9302186

Uyarlanir Yerel Bagli Katman Kullanan Dikkat Tabanli Derin Ag ile Sesli Komut Tanima. / Turkan, Yasemin; Tek, F. Boray.
2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. 9302186 (2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

T1 - Uyarlanir Yerel Bagli Katman Kullanan Dikkat Tabanli Derin Ag ile Sesli Komut Tanima

AU - Turkan, Yasemin

AU - Tek, F. Boray

PY - 2020/10/5

Y1 - 2020/10/5

N2 - Speech command recognition is an active research topic associated with the human-machine interface. Such problems can be successfully solved with attention-based deep networks. In this study, we improved one of the existing attentionbased deep network methods by using an adaptive locally connected (focused) layer. In the experiments we used Google and Kaggle datasets, which were also used in the reference. We observed that the recognition results can be improved significantly (2.6%) by the attention based deep network which uses adaptive locally connected layers.

AB - Speech command recognition is an active research topic associated with the human-machine interface. Such problems can be successfully solved with attention-based deep networks. In this study, we improved one of the existing attentionbased deep network methods by using an adaptive locally connected (focused) layer. In the experiments we used Google and Kaggle datasets, which were also used in the reference. We observed that the recognition results can be improved significantly (2.6%) by the attention based deep network which uses adaptive locally connected layers.

KW - adaptive locally connected neuron

KW - artificial neural networks

KW - attention

KW - speech command recognition

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U2 - 10.1109/SIU49456.2020.9302186

DO - 10.1109/SIU49456.2020.9302186

M3 - Konferans katkısı

AN - SCOPUS:85100315398

T3 - 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings

BT - 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 28th Signal Processing and Communications Applications Conference, SIU 2020

Y2 - 5 October 2020 through 7 October 2020

ER -

Turkan Y, Tek FB. Uyarlanir Yerel Bagli Katman Kullanan Dikkat Tabanli Derin Ag ile Sesli Komut Tanima. In 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. 9302186. (2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings). doi: 10.1109/SIU49456.2020.9302186

Uyarlanir Yerel Bagli Katman Kullanan Dikkat Tabanli Derin Ag ile Sesli Komut Tanima

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

Keywords

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap