Investigation of the Effect of LSTM Hyperparameters on Speech Recognition Performance
Küçük Resim Yok
Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
With the recent advances in hardware technologies and computational methods, computers became more powerful for analyzingdifficult tasks, such as speech recognition and image processing. Speech recognition is the task of extraction of text representation ofa speech signal using computational or analytical methods. Speech recognition is a challenging problem due to variations in accents and languages, powerful hardware requirements, big dataset needs for generating accurate models, and environmental factors thataffect signal quality. Recently, with the increasing processing ability of hardware devices, such as Graphical Processing Units, deeplearning methods became more prevalent and state-of-the-art method for speech recognition, especially Recurrent Neural Networks(RNNs) and Long-Short Term Memory (LSTMs) networks which is a variant of RNNs. In the literature, RNNs and LSTMs are usedfor speech recognition and the applications of speech recognition with various parameters, i.e. number of layers, number of hiddenunits, and batch size. It is not investigated that how the parameter values of the literature are selected and whether these values couldbe used in future studies. In this study, we investigated the effect of LSTMs hyperparameters on speech recognition performance interms of error rates and deep architecture cost. Each parameter is investigated separately while other parameters remain constant andthe effect of each parameter is observed on a speech corpus. Experimental results show that each parameter has its specific values forthe selected number of training instances to provide lower error rates and better speech recognition performance. It is shown in thisstudy that before selecting appropriate values for each LSTM parameters, there should be several experiments performed on thespeech corpus to find the most eligible value for each parameter.
Açıklama
Anahtar Kelimeler
Bilgisayar Bilimleri, Yazılım Mühendisliği, Bilgisayar Bilimleri, Sibernitik, Bilgisayar Bilimleri, Bilgi Sistemleri, Bilgisayar Bilimleri, Donanım ve Mimari, Bilgisayar Bilimleri, Teori ve Metotlar, Akustik, Bilgisayar Bilimleri, Yapay Zeka
Kaynak
Avrupa Bilim ve Teknoloji Dergisi
WoS Q Değeri
Scopus Q Değeri
Cilt
0
Sayı
Ejosat Özel Sayı 2020 (ARACONF)
