Samba-asr state-of-the-art speech recognition leveraging structured
  state-space models
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.
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Syed Abdul Gaffar Shakhadri
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2025-02-26T05:52:16.691Z
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Syed Abdul Gaffar Shakhadri
user
SyedAbdul
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Kruthika KR
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2025-01-07T05:36:04.622Z
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Kruthika
user
kruthika
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Kartik Basavaraj Angadi
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2025-01-07T08:41:40.099Z
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Kartik Basavaraj Angadi
user
Kartik-angadi
Samba-asr state-of-the-art speech recognition leveraging structured
  state-space models
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.
Samba ASR, using the Mamba architecture based on state-space models, surpasses transformer-based models in ASR tasks with better performance and efficiency.
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