282 citations · 700 across the 18 of their papers we have counts for
5 papers · 2 filters
On using 2D sequence-to-sequence models for speech recognition
Parnia Bahar, Albert Zeyer, Ralf Schlüter +1
Attention-based sequence-to-sequence models have shown promising results in automatic speech recognition. Using these architectures, one-dimensional input and output sequences are…
On Using SpecAugment for End-to-End Speech Translation
Parnia Bahar, Albert Zeyer, Ralf Schlüter +1
This work investigates a simple data augmentation technique, SpecAugment, for end-to-end speech translation. SpecAugment is a low-cost implementation method applied directly to the…
Cumulative Adaptation for BLSTM Acoustic Models
Markus Kitza, Pavel Golik, Ralf Schlüter +1
This paper addresses the robust speech recognition problem as an adaptation task. Specifically, we investigate the cumulative application of adaptation methods. A bidirectional Lon…
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…