282 citations · 1.2k across the 80 of their papers we have counts for
21 papers · 1 filter
Adapting Document-Grounded Dialog Systems to Spoken Conversations using Data Augmentation and a Noisy Channel Model
David Thulke, Nico Daheim, Christian Dugast +1
This paper summarizes our submission to Task 2 of the second track of the 10th Dialog System Technology Challenge (DSTC10) "Knowledge-grounded Task-oriented Dialogue Modeling on Sp…
Self-Normalized Importance Sampling for Neural Language Modeling
Zijian Yang, Yingbo Gao, Alexander Gerstenberger +3
To mitigate the problem of having to traverse over the full vocabulary in the softmax normalization of a neural language model, sampling-based training criteria are proposed and in…
Conformer-based Hybrid ASR System for Switchboard Dataset
Mohammad Zeineldeen, Jingjing Xu, Christoph Lüscher +4
The recently proposed conformer architecture has been successfully used for end-to-end automatic speech recognition (ASR) architectures achieving state-of-the-art performance on di…
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…
Investigation on Data Adaptation Techniques for Neural Named Entity Recognition
Evgeniia Tokarchuk, David Thulke, Weiyue Wang +2
Data processing is an important step in various natural language processing tasks. As the commonly used datasets in named entity recognition contain only a limited number of sample…
Efficient Sequence Training of Attention Models using Approximative Recombination
Nils-Philipp Wynands, Wilfried Michel, Jan Rosendahl +2
Sequence discriminative training is a great tool to improve the performance of an automatic speech recognition system. It does, however, necessitate a sum over all possible word se…
On Language Model Integration for RNN Transducer based Speech Recognition
Wei Zhou, Zuoyun Zheng, Ralf Schlüter +1
The mismatch between an external language model (LM) and the implicitly learned internal LM (ILM) of RNN-Transducer (RNN-T) can limit the performance of LM integration such as simp…
Towards Reinforcement Learning for Pivot-based Neural Machine Translation with Non-autoregressive Transformer
Evgeniia Tokarchuk, Jan Rosendahl, Weiyue Wang +4
Pivot-based neural machine translation (NMT) is commonly used in low-resource setups, especially for translation between non-English language pairs. It benefits from using high res…
Integrated Training for Sequence-to-Sequence Models Using Non-Autoregressive Transformer
Evgeniia Tokarchuk, Jan Rosendahl, Weiyue Wang +4
Complex natural language applications such as speech translation or pivot translation traditionally rely on cascaded models. However, cascaded models are known to be prone to error…
Cascaded Span Extraction and Response Generation for Document-Grounded Dialog
Nico Daheim, David Thulke, Christian Dugast +1
This paper summarizes our entries to both subtasks of the first DialDoc shared task which focuses on the agent response prediction task in goal-oriented document-grounded dialogs.…
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…
On Sampling-Based Training Criteria for Neural Language Modeling
Yingbo Gao, David Thulke, Alexander Gerstenberger +3
As the vocabulary size of modern word-based language models becomes ever larger, many sampling-based training criteria are proposed and investigated. The essence of these sampling…
Acoustic Data-Driven Subword Modeling for End-to-End Speech Recognition
Wei Zhou, Mohammad Zeineldeen, Zuoyun Zheng +2
Subword units are commonly used for end-to-end automatic speech recognition (ASR), while a fully acoustic-oriented subword modeling approach is somewhat missing. We propose an acou…
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…
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…
Comparing the Benefit of Synthetic Training Data for Various Automatic Speech Recognition Architectures
Nick Rossenbach, Mohammad Zeineldeen, Benedikt Hilmes +2
Recent publications on automatic-speech-recognition (ASR) have a strong focus on attention encoder-decoder (AED) architectures which tend to suffer from over-fitting in low resourc…
On Architectures and Training for Raw Waveform Feature Extraction in ASR
Peter Vieting, Christoph Lüscher, Wilfried Michel +2
With the success of neural network based modeling in automatic speech recognition (ASR), many studies investigated acoustic modeling and learning of feature extractors directly bas…
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…
Towards Consistent Hybrid HMM Acoustic Modeling
Tina Raissi, Eugen Beck, Ralf Schlüter +1
High-performance hybrid automatic speech recognition (ASR) systems are often trained with clustered triphone outputs, and thus require a complex training pipeline to generate the c…
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…
Efficient Retrieval Augmented Generation from Unstructured Knowledge for Task-Oriented Dialog
David Thulke, Nico Daheim, Christian Dugast +1
This paper summarizes our work on the first track of the ninth Dialog System Technology Challenge (DSTC 9), "Beyond Domain APIs: Task-oriented Conversational Modeling with Unstruct…