World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
54
Citations
10270
World Ranking
4621
National Ranking
2147

Overview

Pedro J. Moreno is affiliated with Google in the United States and has a research focus primarily within the field of Computer Science. Their scholarly output spans several subfields, notably Artificial Intelligence, Signal Processing, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, and Computational Theory and Mathematics.

Their main research topics include Speech Recognition and Synthesis, Natural Language Processing Techniques, Topic Modeling, Music and Audio Processing, Speech and Dialogue Systems, Speech and Audio Processing, and Multimodal Machine Learning Applications.

Recent papers authored by Pedro J. Moreno cover various aspects of speech processing and related multimodal learning approaches. These include:

  • MAESTRO: Matched Speech Text Representations through Modality Matching, 2022, Interspeech 2022
  • Injecting Text in Self-Supervised Speech Pretraining, 2021, 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)
  • Tts4pretrain 2.0: Advancing the use of Text and Speech in ASR Pretraining with Consistency and Contrastive Losses, 2022, ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • Modular Hybrid Autoregressive Transducer, 2023, 2022 IEEE Spoken Language Technology Workshop (SLT)
  • Maestro-U: Leveraging Joint Speech-Text Representation Learning for Zero Supervised Speech ASR, 2023, 2022 IEEE Spoken Language Technology Workshop (SLT)

Pedro J. Moreno frequently publishes in venues such as arXiv (Cornell University), the IEEE Spoken Language Technology Workshop (SLT), ICASSP, Interspeech, and the IEEE Automatic Speech Recognition and Understanding Workshop (ASRU). Their publication count is particularly notable in arXiv and SLT.

  • arXiv (Cornell University)
  • 2022 IEEE Spoken Language Technology Workshop (SLT)
  • ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • Interspeech 2022
  • 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)

Collaborative work is an integral component of their research output, with frequent coauthors including Bhuvana Ramabhadran, Gary Wang, Zhehuai Chen, Andrew Rosenberg, and Yu Zhang.

  • Bhuvana Ramabhadran
  • Gary Wang
  • Zhehuai Chen
  • Andrew Rosenberg
  • Yu Zhang

Best Publications

  • Supervised Learning of Semantic Classes for Image Annotation and Retrieval

    G. Carneiro;A.B. Chan;P.J. Moreno;N. Vasconcelos

  • A vector Taylor series approach for environment-independent speech recognition

    P.J. Moreno;B. Raj;R.M. Stern

  • A Kullback-Leibler Divergence Based Kernel for SVM Classification in Multimedia Applications

    Pedro J. Moreno;Purdy P. Ho;Nuno Vasconcelos

  • Speech recognition in noisy environments

    Pedro J. Moreno

  • Bridging the Gap: Query by Semantic Example

    Nikhil Rasiwasia;P.J. Moreno;N. Vasconcelos

  • Automatic language identification using deep neural networks

    Ignacio Lopez-Moreno;Javier Gonzalez-Dominguez;Oldrich Plchot;David Martinez

  • Automatische aktualisierung eines sprachmodells

    Michael H. Cohen;Shumeet Baluja;Pedro J. Moreno

  • On the use of support vector machines for phonetic classification

    P. Clarkson;P.J. Moreno

  • Multilingual Speech Recognition with a Single End-to-End Model

    Shubham Toshniwal;Tara N. Sainath;Ron J. Weiss;Bo Li

  • Topic segmentation with an aspect hidden Markov model

    David M. Blei;Pedro J. Moreno

  • Method for refining time alignments of closed captions

    Jean-Manuel Van Thong;Pedro Moreno

  • Automatic Language Identification using Long Short-Term Memory Recurrent Neural Networks

    Javier Gonzalez-Dominguez;Ignacio Lopez-Moreno;Hasim Sak;Joaquin Gonzalez-Rodriguez

  • Vocabulary independent speech decoder system and method using subword units

    Jean-Manuel Van Thong;Pedro Moreno;Edward Whittaker

  • A recursive algorithm for the forced alignment of very long audio segments.

    Pedro J. Moreno;Christopher F. Joerg;Jean-Manuel Van Thong;Oren Glickman

  • A Study of Musical Instrument Classification Using Gaussian Mixture Models and Support Vector Machines

    Janet Marques;Pedro J. Moreno

  • Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages

    Unknown

  • Using speech recognition to determine advertisements relevant to audio content and/or audio content relevant to advertisements

    Hua Yu;Pedro Moreno

  • Semantic analysis of song lyrics

    B. Logan;A. Kositsky;P. Moreno

  • From Audio to Semantics: Approaches to End-to-End Spoken Language Understanding

    Parisa Haghani;Arun Narayanan;Michiel Bacchiani;Galen Chuang

  • Using the Fisher kernel method for Web audio classification

    P.J. Moreno;R. Rifkin

  • Speech Recognition with Augmented Synthesized Speech

    Andrew Rosenberg;Yu Zhang;Bhuvana Ramabhadran;Ye Jia

Frequent Co-Authors

Richard M. Stern
Richard M. Stern Carnegie Mellon University
Bhiksha Raj
Bhiksha Raj Carnegie Mellon University
Bhuvana Ramabhadran
Bhuvana Ramabhadran Google (United States)
Nuno Vasconcelos
Nuno Vasconcelos University of California, San Diego
Mehryar Mohri
Mehryar Mohri Google (United States)
Michiel Bacchiani
Michiel Bacchiani Google (United States)
Alejandro Acero
Alejandro Acero Apple (United States)
Antoni B. Chan
Antoni B. Chan City University of Hong Kong
Shumeet Baluja
Shumeet Baluja Google (United States)

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