184 citations · 198 across the 5 of their papers we have counts for
4 papers · 1 filter
Language Modeling with Deep Transformers
Kazuki Irie, Albert Zeyer, Ralf Schlüter +1
We explore deep autoregressive Transformer models in language modeling for speech recognition. We focus on two aspects. First, we revisit Transformer model configurations specifica…
RWTH ASR Systems for LibriSpeech: Hybrid vs Attention -- w/o Data Augmentation
Christoph Lüscher, Eugen Beck, Kazuki Irie +5
We present state-of-the-art automatic speech recognition (ASR) systems employing a standard hybrid DNN/HMM architecture compared to an attention-based encoder-decoder design for th…
On the Choice of Modeling Unit for Sequence-to-Sequence Speech Recognition
Kazuki Irie, Rohit Prabhavalkar, Anjuli Kannan +3
In conventional speech recognition, phoneme-based models outperform grapheme-based models for non-phonetic languages such as English. The performance gap between the two typically…
Improved training of end-to-end attention models for speech recognition
Albert Zeyer, Kazuki Irie, Ralf Schlüter +1
Sequence-to-sequence attention-based models on subword units allow simple open-vocabulary end-to-end speech recognition. In this work, we show that such models can achieve competit…