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![The IBM 2015 English Conversational Telephone
Speech Recognition System[Saon, et al., 2015]
9
⾳音声データ
特徴ベクトル列列
...](https://image.slidesharecdn.com/random-150716035035-lva1-app6891/75/-9-2048.jpg?cb=1666255645)
![ネットワーク構成
10
l 2系統のニューラルネットワーク
- CNNを含む
- 線形レイヤのみ
l 出⼒力力はHMMの状態(state)
etworks [22] which
0-dimensional FM-
100-dimen...](https://image.slidesharecdn.com/random-150716035035-lva1-app6891/75/-10-2048.jpg?cb=1666255645)
![Discriminative Method for Recurrent Neural
Network Language Models [Tachioka, et al., 2015]
13
⾳音声データ
特徴ベクトル...](https://image.slidesharecdn.com/random-150716035035-lva1-app6891/75/-13-2048.jpg?cb=1666255645)
![Discriminative Method for Recurrent Neural
Network Language Models [Tachioka, et al., 2015]
l ⽇日本語話し⾔言葉葉コー...](https://image.slidesharecdn.com/random-150716035035-lva1-app6891/75/-14-2048.jpg?cb=1666255645)
![Deep Speech: Scaling up end-‐‑‒to-‐‑‒end speech
recognition [Hannun, et al., 2014]
l Baidu Researchのグループが発表
...](https://image.slidesharecdn.com/random-150716035035-lva1-app6891/75/-21-2048.jpg?cb=1666255645)
![ネットワーク構成
Once we have computed a prediction for P(ct|x), we compute the CTC loss [13] L(ˆy, y) to measure
the error in pre...](https://image.slidesharecdn.com/random-150716035035-lva1-app6891/75/-22-2048.jpg?cb=1666255645)
![Connectionist Temporal Classification(CTC)
[Graves, et al., 2006]
l ⼊入⼒力力と出⼒力力の系列列⻑⾧長が違う時に⽤用いられる損失関数
l 任意のRNNやLS...](https://image.slidesharecdn.com/random-150716035035-lva1-app6891/75/-23-2048.jpg?cb=1666255645)
