Samba-asr state-of-the-art speech recognition leveraging structured state-space models (2501.02832v1)

Abstract: 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: State-of-the-Art Speech Recognition Leveraging Structured State-Space Models

The research paper introduces Samba-ASR, a pioneering approach in Automatic Speech Recognition (ASR) leveraging Structured State-Space Models (SSMs) through the innovative Mamba architecture. This novel architecture eschews traditional transformer-based self-attention mechanisms, offering an alternative that effectively models both local and global temporal dependencies via efficient state-space dynamics. The dominance of transformer models in ASR is well-established, especially given their ability to process sequences through self-attention. However, they are challenged by quadratic scaling with input length and an impaired capacity to handle long-range dependencies efficiently. Samba-ASR addresses these limitations, achieving superior accuracy and computational efficiency.

Architectural Advancements

Samba-ASR capitalizes on the Mamba architecture, leveraging it for both its encoder and decoder, which diverges significantly from transformer-based designs by eliminating the attention mechanism entirely. Mamba employs input-dependent state-space dynamics to achieve more efficient sequence modeling, notably overcoming the static nature of Linear Time-Invariant (LTI) approaches traditionally employed in SSMs. The architecture is tuned for sequence modeling, excelling in capturing dependencies that are temporally distant and context-heavy, making it particularly effective for audio processing.

The innovation in the Mamba architecture includes the use of selective recurrence and parameter optimization, which provides the computational framework necessary for efficiently handling variability in sequence data. This adaptability is particularly beneficial in noisy environments or where input sequences are varied in length — a known complication for transformer models.

Experimental Evaluation and Results

The empirical results shared in the paper underscore Samba-ASR's efficacy. The system outperforms existing open-source transformer-based ASR models across a range of benchmarks, achieving noteworthy improvements in Word Error Rate (WER). The paper reports significantly enhanced performance on datasets such as Gigaspeech, LibriSpeech Clean/Other, and SPGISpeech. Importantly, Samba-ASR maintains its high accuracy even in low-resource conditions, solidifying its robustness and wide applicability. This performance is achieved alongside reduced inference latency and training time, which marks a significant advantage in the field of ASR.

Implications and Future Directions

Samba-ASR demonstrates the substantial potential of adopting state-space models for speech recognition tasks, setting a novel benchmark for future developments in this domain. The success of Mamba-based state-space dynamics suggests exciting opportunities for further exploration, particularly in extending the approach to other sequence modeling challenges beyond ASR, such as language and visual tasks. It also paves the way for developing new models that can capitalize on the efficiency and scalability benefits conferred by these architectures.

Furthermore, since ASR systems are increasingly deployed in real-time and resource-constrained environments, the improvements in efficiency and processing speed offered by Samba-ASR could significantly influence future applications. The potential to maintain high performance with lower computational overhead might lead to broader adoption in varied devices and applications, potentially transforming industries reliant on speech technology.

In summary, the paper presents Samba-ASR as a formidable alternative to transformer-based speech recognition systems. It highlights both the theoretical and practical value of the Mamba architecture, showcasing its unique contributions to state-space modeling and its application in ASR. The scope for future research is vast, promising further refinements and applications in the evolving landscape of AI-driven speech processing.