282 citations · 1.2k across the 80 of their papers we have counts for
21 papers · 1 filter
Generating Synthetic Audio Data for Attention-Based Speech Recognition Systems
Nick Rossenbach, Albert Zeyer, Ralf Schlüter +1
Recent advances in text-to-speech (TTS) led to the development of flexible multi-speaker end-to-end TTS systems. We extend state-of-the-art attention-based automatic speech recogni…
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…
A Comparative Study on End-to-end Speech to Text Translation
Parnia Bahar, Tobias Bieschke, Hermann Ney
Recent advances in deep learning show that end-to-end speech to text translation model is a promising approach to direct the speech translation field. In this work, we provide an o…
ELoPE: Fine-Grained Visual Classification with Efficient Localization, Pooling and Embedding
Harald Hanselmann, Hermann Ney
The task of fine-grained visual classification (FGVC) deals with classification problems that display a small inter-class variance such as distinguishing between different bird spe…
Exploring Kernel Functions in the Softmax Layer for Contextual Word Classification
Yingbo Gao, Christian Herold, Weiyue Wang +1
Prominently used in support vector machines and logistic regressions, kernel functions (kernels) can implicitly map data points into high dimensional spaces and make it easier to l…
When and Why is Document-level Context Useful in Neural Machine Translation?
Yunsu Kim, Duc Thanh Tran, Hermann Ney
Document-level context has received lots of attention for compensating neural machine translation (NMT) of isolated sentences. However, recent advances in document-level NMT focus…
Pivot-based Transfer Learning for Neural Machine Translation between Non-English Languages
Yunsu Kim, Petre Petrov, Pavel Petrushkov +2
We present effective pre-training strategies for neural machine translation (NMT) using parallel corpora involving a pivot language, i.e., source-pivot and pivot-target, leading to…
uniblock: Scoring and Filtering Corpus with Unicode Block Information
Yingbo Gao, Weiyue Wang, Hermann Ney
The preprocessing pipelines in Natural Language Processing usually involve a step of removing sentences consisted of illegal characters. The definition of illegal characters and th…
Comparison of Lattice-Free and Lattice-Based Sequence Discriminative Training Criteria for LVCSR
Wilfried Michel, Ralf Schlüter, Hermann Ney
Sequence discriminative training criteria have long been a standard tool in automatic speech recognition for improving the performance of acoustic models over their maximum likelih…
LSTM Language Models for LVCSR in First-Pass Decoding and Lattice-Rescoring
Eugen Beck, Wei Zhou, Ralf Schlüter +1
LSTM based language models are an important part of modern LVCSR systems as they significantly improve performance over traditional backoff language models. Incorporating them effi…
Generalizing Back-Translation in Neural Machine Translation
Miguel Graça, Yunsu Kim, Julian Schamper +2
Back-translation - data augmentation by translating target monolingual data - is a crucial component in modern neural machine translation (NMT). In this work, we reformulate back-t…
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…
Learning Bilingual Sentence Embeddings via Autoencoding and Computing Similarities with a Multilayer Perceptron
Yunsu Kim, Hendrik Rosendahl, Nick Rossenbach +3
We propose a novel model architecture and training algorithm to learn bilingual sentence embeddings from a combination of parallel and monolingual data. Our method connects autoenc…
Effective Cross-lingual Transfer of Neural Machine Translation Models without Shared Vocabularies
Yunsu Kim, Yingbo Gao, Hermann Ney
Transfer learning or multilingual model is essential for low-resource neural machine translation (NMT), but the applicability is limited to cognate languages by sharing their vocab…
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…
Analysis of Deep Clustering as Preprocessing for Automatic Speech Recognition of Sparsely Overlapping Speech
Tobias Menne, Ilya Sklyar, Ralf Schlüter +1
Significant performance degradation of automatic speech recognition (ASR) systems is observed when the audio signal contains cross-talk. One of the recently proposed approaches to…
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…
Improving Unsupervised Word-by-Word Translation with Language Model and Denoising Autoencoder
Yunsu Kim, Jiahui Geng, Hermann Ney
Unsupervised learning of cross-lingual word embedding offers elegant matching of words across languages, but has fundamental limitations in translating sentences. In this paper, we…
Unsupervised Training for Large Vocabulary Translation Using Sparse Lexicon and Word Classes
Yunsu Kim, Julian Schamper, Hermann Ney
We address for the first time unsupervised training for a translation task with hundreds of thousands of vocabulary words. We scale up the expectation-maximization (EM) algorithm t…
A Comparative Study on Vocabulary Reduction for Phrase Table Smoothing
Yunsu Kim, Andreas Guta, Joern Wuebker +1
This work systematically analyzes the smoothing effect of vocabulary reduction for phrase translation models. We extensively compare various word-level vocabularies to show that th…