282 citations · 700 across the 18 of their papers we have counts for
12 papers · 1 filter
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…
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…
The Impact of ASR on the Automatic Analysis of Linguistic Complexity and Sophistication in Spontaneous L2 Speech
Yu Qiao, Wei Zhou, Elma Kerz +1
In recent years, automated approaches to assessing linguistic complexity in second language (L2) writing have made significant progress in gauging learner performance, predicting h…
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…
Phoneme Based Neural Transducer for Large Vocabulary Speech Recognition
Wei Zhou, Simon Berger, Ralf Schlüter +1
To join the advantages of classical and end-to-end approaches for speech recognition, we present a simple, novel and competitive approach for phoneme-based neural transducer modeli…
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…
Speaker Adapted Beamforming for Multi-Channel Automatic Speech Recognition
Tobias Menne, Ralf Schlüter, Hermann Ney
This paper presents, in the context of multi-channel ASR, a method to adapt a mask based, statistically optimal beamforming approach to a speaker of interest. The beamforming vecto…
Improved training of end-to-end attention models for speech recognition
Albert Zeyer, Kazuki Irie, Ralf Schlüter +1
Sequence-to-sequence attention-based models on subword units allow simple open-vocabulary end-to-end speech recognition. In this work, we show that such models can achieve competit…