M.Eng. in Artificial Intelligence · Science Tokyo

I’m a speech and audio ML researcher with a Master of Engineering in Artificial Intelligence from the Institute of Science Tokyo. My research focuses on speech separation, neural audio codecs, and robust speech recognition.

Outside research, I enjoy photography and am a cinematography enthusiast. You can find my photos and videos in Photo.

Portrait of Phuong Dinh

News

2026

  1. I presented my paper at Interspeech 2026.

  2. I was selected to receive the diploma on behalf of all students in the Department of Computer Science, School of Computing, at graduation.

  3. My paper (RVQ-Grid) was accepted at Interspeech 2026.

    I presented our work on cross-layer RVQ processing at 音学シンポジウム.

  4. Our Science Tokyo MCoRec system paper appeared in the CHiME 2026 proceedings.

2025

  1. I joined a research collaboration between Shinoda Laboratory and NEC’s research lab in Tokyo.

2024

  1. I started my M.Eng. in Artificial Intelligence at Science Tokyo.

2021

  1. I joined LINE Technology Vietnam as a software engineer.

2020

  1. I graduated from USTH and joined ABIVIN as a software engineer.

Publication

RVQ-Grid architecture: frozen codec pipeline, stacked RVQ vectors and dual-axis recurrent separator (Figure 1).

Improving Audio Codec-based Speech Separation By Stacking Residual Vector Quantization Layers

Nhu Minh Phuong Dinh, Roland Hartanto, Koichi Shinoda

Interspeech 2026 · pp. 7153–7157

RVQ-Grid explores the structure of residual vector quantization layers for speech separation in neural audio codec representations.

inputmodeloutput

Cross-layer recurrent processing of Residual Vector Quantization layers for Audio Codec-based speech separation

Nhu Minh Phuong Dinh, Roland Hartanto, Koichi Shinoda

IEICE Technical Report · SP2026-23 · pp. 125–129

Cross-layer recurrent processing preserves the coarse-to-fine structure of RVQ layers for codec-based speech separation.

CHiME-9 MCoRec architecture: baseline and proposed audio-visual ASR system with an auxiliary target-speaker extraction training path (Figure 1).

Science Tokyo CHiME-9 MCoRec System Description

Roland Hartanto, Daichi Nitsu, Nhu Minh Phuong Dinh, Koichi Shinoda

CHiME 2026 · pp. 21–25

Joint training combines audio-visual speech recognition and target speaker extraction to handle overlapping conversations.

Education

  1. M.Eng. in Artificial Intelligence

    Institute of Science Tokyo

  2. B.Sc. in Information and Communication Technology

    University of Science and Technology of Hanoi

Experience

  1. Research Assistant

    Shinoda Laboratory

  2. Software Engineer

    LINE Technology Vietnam

  3. Software Engineer

    ABIVIN

Skills

Research
  • Speech separation
  • Neural audio codecs
  • Deep learning
  • PyTorch
Engineering
  • Python
  • Java
  • Spring Framework
  • Data processing & analytics
  • Docker
  • Kubernetes
  • CI/CD
Languages
  • 🇻🇳 Vietnamese · Native
  • 🇬🇧 English · Full working proficiency
  • 🇯🇵 Japanese · Basic
  • 🇫🇷 French · Basic

Awards

  1. LINE Engineering Culture winner

    LINE

  2. LINE Technology Vietnam Awards winner

    LINE Technology Vietnam

  3. Excellent Student Scholarship (Fully Funded)

    University of Science and Technology of Hanoi