activity
20162026
most citedImproved training of end-to-end attention models for speech recognition

282 citations · 950 across the 25 of their papers we have counts for

collaborators
Showing 2021Show all

5 papers · 1 filter

cs.LG2021★ 18 cited

Why does CTC result in peaky behavior?

Albert Zeyer, Ralf Schlüter, Hermann Ney

The peaky behavior of CTC models is well known experimentally. However, an understanding about why peaky behavior occurs is missing, and whether this is a good property. We provide…

cs.CL2021★ 4 cited

Equivalence of Segmental and Neural Transducer Modeling: A Proof of Concept

Wei Zhou, Albert Zeyer, André Merboldt +2

With the advent of direct models in automatic speech recognition (ASR), the formerly prevalent frame-wise acoustic modeling based on hidden Markov models (HMM) diversified into a n…

cs.CL2021

Investigating Methods to Improve Language Model Integration for Attention-based Encoder-Decoder ASR Models

Mohammad Zeineldeen, Aleksandr Glushko, Wilfried Michel +3

Attention-based encoder-decoder (AED) models learn an implicit internal language model (ILM) from the training transcriptions. The integration with an external LM trained on much m…

cs.CL2021

Librispeech Transducer Model with Internal Language Model Prior Correction

Albert Zeyer, André Merboldt, Wilfried Michel +2

We present our transducer model on Librispeech. We study variants to include an external language model (LM) with shallow fusion and subtract an estimated internal LM. This is just…

cs.CL2021★ 7 cited

A study of latent monotonic attention variants

Albert Zeyer, Ralf Schlüter, Hermann Ney

End-to-end models reach state-of-the-art performance for speech recognition, but global soft attention is not monotonic, which might lead to convergence problems, to instability, t…