World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
50
Citations
11861
World Ranking
5552
National Ranking
258

Overview

Ralf Schlüter is affiliated with RWTH Aachen University in Germany and has made significant contributions to the field of computer science, with a particular focus on artificial intelligence and signal processing. Their research work spans various subfields, including electrical and electronic engineering, computer vision and pattern recognition, and control and systems engineering.

The primary areas of study for Schlüter center on speech recognition and synthesis, music and audio processing, and broader speech and audio processing techniques. They also explore natural language processing techniques, topic modeling, speech and dialogue systems, and advanced memory and neural computing.

The scientist's publication record includes involvement in frequent venues such as arXiv (Cornell University), where they have published 56 papers, as well as contributions to prominent conferences and workshops like ICASSP 2022, Interspeech 2022, the 2022 IEEE Spoken Language Technology Workshop (SLT), and the 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU).

Highlighted recent papers authored by or involving Schlüter include:

  • "End-to-End Speech Recognition: A Survey" (2023), IEEE/ACM Transactions on Audio Speech and Language Processing
  • "Conformer-Based Hybrid ASR System For Switchboard Dataset" (2022), ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • "Why does CTC result in peaky behavior?" (2021), arXiv (Cornell University)
  • "On Language Model Integration for RNN Transducer Based Speech Recognition" (2022), ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • "Efficient Training of Neural Transducer for Speech Recognition" (2022), Interspeech 2022

Frequent co-authors collaborating with Schlüter include Hermann Ney, Wei Zhou, Mohammad Zeineldeen, Wilfried Michel, and Christoph Lüscher.

The collaborative network and choice of publication venues indicate active engagement in the international speech recognition and audio processing research communities. The scientist's work contributes to a deeper understanding and development of models and techniques that address various challenges in speech and language technology.

Best Publications

  • LSTM Neural Networks for Language Modeling.

    Martin Sundermeyer;Ralf Schlüter;Hermann Ney

  • Confidence measures for large vocabulary continuous speech recognition

    F. Wessel;R. Schluter;K. Macherey;H. Ney

  • From feedforward to recurrent LSTM neural networks for language modeling

    Martin Sundermeyer;Hermann Ney;Ralf Schlüter

  • Computing Mel-frequency cepstral coefficients on the power spectrum

    S. Molau;M. Pitz;R. Schluter;H. Ney

  • Improved Training of End-to-end Attention Models for Speech Recognition

    Albert Zeyer;Kazuki Irie;Ralf Schlüter;Hermann Ney

  • Vocal tract normalization equals linear transformation in cepstral space.

    Michael Pitz;Sirko Molau;Ralf Schlüter;Hermann Ney

  • RWTH ASR Systems for LibriSpeech: Hybrid vs Attention.

    Christoph Lüscher;Eugen Beck;Kazuki Irie;Markus Kitza

  • Language Modeling with Deep Transformers

    Kazuki Irie;Albert Zeyer;Ralf Schlüter;Hermann Ney

  • Acoustic modeling with deep neural networks using raw time signal for LVCSR.

    Zoltán Tüske;Pavel Golik;Ralf Schlüter;Hermann Ney

  • A Comparison of Transformer and LSTM Encoder Decoder Models for ASR

    Albert Zeyer;Parnia Bahar;Kazuki Irie;Ralf Schluter

  • A comprehensive study of deep bidirectional LSTM RNNS for acoustic modeling in speech recognition

    Albert Zeyer;Patrick Doetsch;Paul Voigtlaender;Ralf Schluter

  • Gammatone Features and Feature Combination for Large Vocabulary Speech Recognition

    R. Schluter;L. Bezrukov;H. Wagner;H. Ney

  • Using phase spectrum information for improved speech recognition performance

    R. Schluter;H. Ney

  • RWTH ASR Systems for LibriSpeech: Hybrid vs Attention - w/o Data Augmentation.

    Christoph Lüscher;Eugen Beck;Kazuki Irie;Markus Kitza

  • Using word probabilities as confidence measures

    F. Wessel;K. Macherey;R. Schluter

  • Comparison of feedforward and recurrent neural network language models

    M. Sundermeyer;I. Oparin;J.-L Gauvain;B. Freiberg

  • The RWTH aachen university open source speech recognition system.

    David Rybach;Christian Gollan;Georg Heigold;Björn Hoffmeister

  • Convolutional Neural Networks for Acoustic Modeling of Raw Time Signal in LVCSR

    Pavel Golik;Zoltán Tüske;Ralf Schlüter;Hermann Ney

  • Comparison of discriminative training criteria and optimization methods for speech recognition

    Ralf Schlüter;Wolfgang Macherey;Boris Müller;Hermann Ney

  • LSTM, GRU, Highway and a Bit of Attention: An Empirical Overview for Language Modeling in Speech Recognition.

    Kazuki Irie;Zoltán Tüske;Tamer Alkhouli;Ralf Schlüter

  • Acoustic feature combination for robust speech recognition

    A. Zolnay;R. Schluter;H. Ney

Frequent Co-Authors

Hermann Ney
Hermann Ney RWTH Aachen University
Georg Heigold
Georg Heigold German Research Centre for Artificial Intelligence
Mark J. F. Gales
Mark J. F. Gales University of Cambridge
Brian Kingsbury
Brian Kingsbury IBM (United States)
Bhuvana Ramabhadran
Bhuvana Ramabhadran Google (United States)
Reinhold Haeb-Umbach
Reinhold Haeb-Umbach University of Paderborn
Michael Picheny
Michael Picheny IBM (United States)
Philip C. Woodland
Philip C. Woodland University of Cambridge
Jean-Luc Gauvain
Jean-Luc Gauvain Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur
Hynek Hermansky
Hynek Hermansky Johns Hopkins University

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