title: DinoSR: Self-Distillation and Online Clustering for Self-supervised
    Speech Representation Learning
summary: In this paper, we introduce self-distillation and online clustering for
  self-supervised speech representation learning (DinoSR) which combines masked
  language modeling, self-distillation, and online clustering. We show that these
  concepts complement each other and result in a strong representation learning
  model for speech. DinoSR first extracts contextualized embeddings from the
  input audio with a teacher network, then runs an online clustering system on
  the embeddings to yield a machine-discovered phone inventory, and finally uses
  the discretized tokens to guide a student network. We show that DinoSR
  surpasses previous state-of-the-art performance in several downstream tasks,
  and provide a detailed analysis of the model and the learned discrete units.
  The source code will be made available after the anonymity period.
paper_authors._id: 646586505cdb9ab50f874fdc
paper_authors.name: Alexander H. Liu
paper_authors._id: 646586505cdb9ab50f874fdd
paper_authors.name: Heng-Jui Chang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-19T10:28:27.902Z
paper_authors._id: 6179f36a2a4e9edab3a95798
paper_authors.avatarUrl: /avatars/fdb5ee0a21934c3b34f481930bfb82b1.svg
paper_authors.fullname: Heng-Jui Chang
paper_authors.type: user
paper_authors.user: vectominist
paper_authors._id: 646586505cdb9ab50f874fde
paper_authors.name: Michael Auli
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-05-26T09:19:25.810Z
paper_authors._id: 6176e9d475f6ae94d250042c
paper_authors.avatarUrl: /avatars/ce0eafa09525b741c22717bbde6c6a16.svg
paper_authors.fullname: Michael Auli
paper_authors.type: user
paper_authors.user: michaelauli
paper_authors._id: 646586505cdb9ab50f874fdf
paper_authors.name: Wei-Ning Hsu
paper_authors._id: 646586505cdb9ab50f874fe0
paper_authors.name: James R. Glass
paper_title: DinoSR: Self-Distillation and Online Clustering for Self-supervised
    Speech Representation Learning
paper_summary: In this paper, we introduce self-distillation and online clustering for
  self-supervised speech representation learning (DinoSR) which combines masked
  language modeling, self-distillation, and online clustering. We show that these
  concepts complement each other and result in a strong representation learning
  model for speech. DinoSR first extracts contextualized embeddings from the
  input audio with a teacher network, then runs an online clustering system on
  the embeddings to yield a machine-discovered phone inventory, and finally uses
  the discretized tokens to guide a student network. We show that DinoSR
  surpasses previous state-of-the-art performance in several downstream tasks,
  and provide a detailed analysis of the model and the learned discrete units.
  The source code will be made available after the anonymity period.
paper_ai_keywords: self-distillation online clustering self-supervised speech representation learning DinoSR masked language modeling teacher network contextualized embeddings machine-discovered phone inventory discretized tokens student network downstream tasks discrete units