World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
50
Citations
13248
World Ranking
5520
National Ranking
2520

Overview

Frank Seide is affiliated with Microsoft in the United States. Their research work primarily spans the field of Computer Science, with a significant focus on Artificial Intelligence and Signal Processing. Their scientific contributions cover areas such as Speech Recognition and Synthesis, Music and Audio Processing, Natural Language Processing Techniques, and Domain Adaptation.

Seide has published extensively in venues like arXiv (Cornell University), Interspeech 2022, and the 2022 IEEE Spoken Language Technology Workshop (SLT). The majority of their publications appear on arXiv, which accounts for a dozen research papers.

Their recent papers include:

  • Speech ReaLLM -- Real-time Streaming Speech Recognition with Multimodal LLMs by Teaching the Flow of Time, 2024, arXiv (Cornell University)
  • Directional Source Separation for Robust Speech Recognition on Smart Glasses, 2023, arXiv (Cornell University)
  • DISGO: Automatic End-to-End Evaluation for Scene Text OCR, 2023, arXiv (Cornell University)
  • An Investigation of Monotonic Transducers for Large-Scale Automatic Speech Recognition, 2023, 2022 IEEE Spoken Language Technology Workshop (SLT)
  • Federated Domain Adaptation for ASR with Full Self-Supervision, 2022, Interspeech 2022

Seide's research topics highlight a concentration on Speech and Audio Processing, including specialized areas such as Speech and Dialogue Systems and Handwritten Text Recognition Techniques. They also engage with contemporary challenges in Domain Adaptation and Few-Shot Learning.

Frequent collaborators in their work include Jay Mahadeokar, Ozlem Kalinli, Christian Fuegen, Junteng Jia, and Niko Moritz, with cooperative publication counts ranging from four to six joint papers per coauthor.

Their research output reflects ongoing contributions to advancing techniques in speech recognition and audio processing, particularly in large-scale and noisy environments. The combination of theoretical and applied research in automatic speech recognition and multimodal language models underscores the breadth and depth of their work.

Best Publications

  • Conversational Speech Transcription Using Context-Dependent Deep Neural Networks.

    Frank Seide;Gang Li;Dong Yu

  • Recent advances in deep learning for speech research at Microsoft

    Li Deng;Jinyu Li;Jui-Ting Huang;Kaisheng Yao

  • 1-bit stochastic gradient descent and its application to data-parallel distributed training of speech DNNs.

    Frank Seide;Hao Fu;Jasha Droppo;Gang Li

  • Feature engineering in Context-Dependent Deep Neural Networks for conversational speech transcription

    Frank Seide;Gang Li;Xie Chen;Dong Yu

  • Achieving Human Parity on Automatic Chinese to English News Translation

    Hany Hassan;Anthony Aue;Chang Chen;Vishal Chowdhary

  • Achieving Human Parity in Conversational Speech Recognition

    Wayne Xiong;Jasha Droppo;Xuedong Huang;Frank Seide

  • Marian: Fast Neural Machine Translation in C++

    Marcin Junczys-Dowmunt;Roman Grundkiewicz;Tomasz Dwojak;Hieu Hoang

  • CNTK: Microsoft's Open-Source Deep-Learning Toolkit

    Frank Seide;Amit Agarwal

  • KL-divergence regularized deep neural network adaptation for improved large vocabulary speech recognition

    Dong Yu;Kaisheng Yao;Hang Su;Gang Li

  • An Introduction to Computational Networks and the Computational Network Toolkit

    Dong Yu;Adam Eversole;Mike Seltzer;Kaisheng Yao

  • The Philips automatic train timetable information system

    Harald Aust;Martin Oerder;Frank Seide;Volker Steinbiss

  • The microsoft 2016 conversational speech recognition system

    W. Xiong;J. Droppo;X. Huang;F. Seide

  • Adaptation of context-dependent deep neural networks for automatic speech recognition

    Kaisheng Yao;Dong Yu;Frank Seide;Hang Su

  • Feature Learning in Deep Neural Networks - Studies on Speech Recognition Tasks

    Dong Yu;Michael L. Seltzer;Jinyu Li;Jui-Ting Huang

  • Toward Human Parity in Conversational Speech Recognition

    Wayne Xiong;Jasha Droppo;Xuedong Huang;Frank Seide

  • Internet search-based television

    Frank T. B. Seide;Lie Lu;Neema M. Moraveji;Roger Peng Yu

  • Improving speech understanding by incorporating database constraints and dialogue history

    F. Seide;B. Rueber;A. Kellner

  • Exploiting sparseness in deep neural networks for large vocabulary speech recognition

    Dong Yu;Frank Seide;Gang Li;Li Deng

  • Error back propagation for sequence training of Context-Dependent Deep NetworkS for conversational speech transcription

    Hang Su;Gang Li;Dong Yu;Frank Seide

  • Conversational speech transcription using context-dependent deep neural networks

    Dong Yu;Frank Seide;Gang Li

Frequent Co-Authors

Dong Yu
Dong Yu Tencent (China)
Li Deng
Li Deng Citadel
Lie Lu
Lie Lu Dolby (United States)
Xuedong Huang
Xuedong Huang Microsoft (United States)
Michael L. Seltzer
Michael L. Seltzer Facebook (United States)
Jasha Droppo
Jasha Droppo Amazon (United States)
Alejandro Acero
Alejandro Acero Apple (United States)
Eric Chang
Eric Chang Microsoft (United States)
Jinyu Li
Jinyu Li Microsoft (United States)

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