title: Samba-asr state-of-the-art speech recognition leveraging structured
    state-space models
summary: We propose Samba ASR, the first state-of-the-art Automatic Speech Recognition
  (ASR) model leveraging the novel Mamba architecture as both encoder and
  decoder, built on the foundation of state-space models (SSMs). Unlike
  transformer-based ASR models, which rely on self-attention mechanisms to
  capture dependencies, Samba ASR effectively models both local and global
  temporal dependencies using efficient state-space dynamics, achieving
  remarkable performance gains. By addressing the limitations of transformers,
  such as quadratic scaling with input length and difficulty in handling
  long-range dependencies, Samba ASR achieves superior accuracy and efficiency.
    Experimental results demonstrate that Samba ASR surpasses existing
  open-source transformer-based ASR models across various standard benchmarks,
  establishing it as the new state of the art in ASR. Extensive evaluations on
  benchmark datasets show significant improvements in Word Error Rate (WER), with
  competitive performance even in low-resource scenarios. Furthermore, the
  computational efficiency and parameter optimization of the Mamba architecture
  make Samba ASR a scalable and robust solution for diverse ASR tasks.
    Our contributions include:
    A new Samba ASR architecture demonstrating the superiority of SSMs over
  transformer-based models for speech sequence processing. A comprehensive
  evaluation on public benchmarks showcasing state-of-the-art performance. An
  analysis of computational efficiency, robustness to noise, and sequence
  generalization. This work highlights the viability of Mamba SSMs as a
  transformer-free alternative for efficient and accurate ASR. By leveraging
  state-space modeling advancements, Samba ASR sets a new benchmark for ASR
  performance and future research.
paper_authors._id: 677cbd43ce51bc60df2c94db
paper_authors.name: Syed Abdul Gaffar Shakhadri
paper_authors.status: extracted_confirmed
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paper_authors.fullname: Syed Abdul Gaffar Shakhadri
paper_authors.type: user
paper_authors.user: SyedAbdul
paper_authors._id: 677cbd43ce51bc60df2c94dc
paper_authors.name: Kruthika KR
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-01-07T05:36:04.622Z
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paper_authors.fullname: Kruthika
paper_authors.type: user
paper_authors.user: kruthika
paper_authors._id: 677cbd43ce51bc60df2c94dd
paper_authors.name: Kartik Basavaraj Angadi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-07T08:41:40.099Z
paper_authors._id: 666a75f9f0d87d9c3b278693
paper_authors.avatarUrl: /avatars/657f2273bff0e136c93f10ad72ce87d8.svg
paper_authors.fullname: Kartik Basavaraj Angadi
paper_authors.type: user
paper_authors.user: Kartik-angadi
paper_title: Samba-asr state-of-the-art speech recognition leveraging structured
    state-space models
paper_summary: We propose Samba ASR, the first state-of-the-art Automatic Speech Recognition
  (ASR) model leveraging the novel Mamba architecture as both encoder and
  decoder, built on the foundation of state-space models (SSMs). Unlike
  transformer-based ASR models, which rely on self-attention mechanisms to
  capture dependencies, Samba ASR effectively models both local and global
  temporal dependencies using efficient state-space dynamics, achieving
  remarkable performance gains. By addressing the limitations of transformers,
  such as quadratic scaling with input length and difficulty in handling
  long-range dependencies, Samba ASR achieves superior accuracy and efficiency.
    Experimental results demonstrate that Samba ASR surpasses existing
  open-source transformer-based ASR models across various standard benchmarks,
  establishing it as the new state of the art in ASR. Extensive evaluations on
  benchmark datasets show significant improvements in Word Error Rate (WER), with
  competitive performance even in low-resource scenarios. Furthermore, the
  computational efficiency and parameter optimization of the Mamba architecture
  make Samba ASR a scalable and robust solution for diverse ASR tasks.
    Our contributions include:
    A new Samba ASR architecture demonstrating the superiority of SSMs over
  transformer-based models for speech sequence processing. A comprehensive
  evaluation on public benchmarks showcasing state-of-the-art performance. An
  analysis of computational efficiency, robustness to noise, and sequence
  generalization. This work highlights the viability of Mamba SSMs as a
  transformer-free alternative for efficient and accurate ASR. By leveraging
  state-space modeling advancements, Samba ASR sets a new benchmark for ASR
  performance and future research.
paper_ai_summary: Samba ASR, using the Mamba architecture based on state-space models, surpasses transformer-based models in ASR tasks with better performance and efficiency.
paper_ai_keywords: Mamba architecture state-space models SSMs self-attention mechanisms temporal dependencies state-of-the-art ASR Word Error Rate (WER) computational efficiency parameter optimization sequence processing sequence generalization