23 citations · 48 across the 13 of their papers we have counts for
5 papers · 1 filter
LLMs and Speech: Integration vs. Combination
Robin Schmitt, Albert Zeyer, Mohammad Zeineldeen +2
In this work, we study different approaches to utilize large language models (LLMs) for automatic speech recognition (ASR). Specifically, we compare the tight integration of an aco…
The Conformer Encoder May Reverse the Time Dimension
Robin Schmitt, Albert Zeyer, Mohammad Zeineldeen +2
We sometimes observe monotonically decreasing cross-attention weights in our Conformer-based global attention-based encoder-decoder (AED) models, Further investigation shows that t…
Chunked Attention-based Encoder-Decoder Model for Streaming Speech Recognition
Mohammad Zeineldeen, Albert Zeyer, Ralf Schlüter +1
We study a streamable attention-based encoder-decoder model in which either the decoder, or both the encoder and decoder, operate on pre-defined, fixed-size windows called chunks.…
A systematic comparison of grapheme-based vs. phoneme-based label units for encoder-decoder-attention models
Mohammad Zeineldeen, Albert Zeyer, Wei Zhou +3
Following the rationale of end-to-end modeling, CTC, RNN-T or encoder-decoder-attention models for automatic speech recognition (ASR) use graphemes or grapheme-based subword units…
A New Training Pipeline for an Improved Neural Transducer
Albert Zeyer, André Merboldt, Ralf Schlüter +1
The RNN transducer is a promising end-to-end model candidate. We compare the original training criterion with the full marginalization over all alignments, to the commonly used max…