Konuşma Tanıma için Kodlayıcı Olarak Paralel Kapılı Tekrarlayan Birim Ağları

Yıl 2022, Sayı: 36, 87 - 90, 31.05.2022

Zekeriya Tüfekci , Gökay Dişken

https://doi.org/10.31590/ejosat.1103714

Öz

Listen, Attend and Spell (LAS) ağı konuşma tanıma için belli bir dil modeline gereksinim duymayan uçtan-uca yaklaşımlardan biridir. İki kısımdan oluşur; akustik öznitelikleri girdi olarak alan kodlayıcı kısmı, kodlayıcı çıkışı ve dikkat mekanizmasına bağlı olarak bir zaman adımında tek bir karakter üreten kod çözümleyici kısmı. Hem kod çözümleyici hem de kodlayıcı kısımlarında çok katmanlı tekrarlayan sinir ağları (RNN) kullanılır. Bu nedenle LAS mimarisi kod çözümleyici için bir RNN ve kodlayıcı için bir başka RNN olarak basitleştirilebilir. Şekilleri ve katman boyutları farklı olabilir. Bu çalışmada, kodlayıcı kısmı için çoklu RNN kullanımının performansını inceledik. Temel alınan LAS ağı 256 gizli boyutu olan bir RNN kullanmaktadır. 128 ve 64 gizli boyutları için 2 ve 4 RNN kullandık. Önerilen yaklaşımın ardındaki ana fikir, RNN’leri verilerdeki farklı örüntülere (bu çalışma için fonemler) odaklamaktır. Kodlayıcının çıkışında bunların çıkışları birleştirilir ve kod çözümleyiciye iletilir. TIMIT veritabanı, performans metriği olarak fonem hata oranı seçilerek bahsedilen ağların performansını karşılaştırmak için kullanılmıştır. Deneysel sonuçlar, önerilen yaklaşımın temek alınan ağdan daha iyi bir performans elde edebileceğini göstermiştir. Ancak RNN’lerin sayısını artırmak daha fazla
iyileşmeyi garanti etmemektedir.

Anahtar Kelimeler

Dikkat ağları, Tekrarlayan sinir ağları, Konuşma tanıma, Timit

Parallel Gated Recurrent Unit Networks as an Encoder for Speech Recognition

Öz

Listen, Attend and Spell (LAS) network is one of the end-to-end approaches for speech recognition, which does not require an explicit language model. It consists of two parts; the encoder part which receives acoustic features as inputs, and the decoder network which produces one character at a time step, based on the encoder output and an attention mechanism. Multi-layer recurrent neural networks (RNN) are used in both decoder and encoder parts. Hence, the LAS architecture can be simplified as one RNN for the decoder, and another RNN for the encoder. Their shapes and layer sizes can be different. In this work, we examined the performance of using multi RNNs for the encoder part. Our baseline LAS network uses an RNN with a hidden size of 256. We used 2 and 4 RNNs with hidden
sizes of 128 and 64 for each case. The main idea behind the proposed approach is to focus the RNNs to different patterns (phonemes in this case) in the data. At the output of the encoder, their outputs are concatenated and fed to the decoder. TIMIT database is used to compare the performance of the mentioned networks, using phoneme error rate as the performance metric. The experimental results showed that proposed approach can achieve a better performance than the baseline network. However, increasing the number of RNNs does not guarantee further improvements.

Anahtar Kelimeler

Attention networks, Recurrent neural networks, Speech recognition, Timit

Kaynakça

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