activity
20192026
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cs.CL2026

AppTek Call-Center Dialogues: A Multi-Accent Long-Form Benchmark for English ASR

Eugen Beck, Sarah Beranek, Uma Moothiringote +4

Evaluating English ASR systems for conversational AI applications remains difficult, as many publicly available corpora are either pre-segmented into short segments, consist of rea…

cs.CL2021

Automatic Learning of Subword Dependent Model Scales

Felix Meyer, Wilfried Michel, Mohammad Zeineldeen +2

To improve the performance of state-of-the-art automatic speech recognition systems it is common practice to include external knowledge sources such as language models or prior cor…

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.CL2019

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…