All-in-One ASR: Unifying Encoder-Decoder Models of CTC, Attention, and Transducer in Dual-Mode ASR
Abstract
This paper proposes a unified framework, All-in-One ASR, that allows a single model to support multiple automatic speech recognition (ASR) paradigms, including connectionist temporal classification (CTC), attention-based encoder-decoder (AED), and Transducer, in both offline and streaming modes. While each ASR architecture offers distinct advantages and trade-offs depending on the application, maintaining separate models for each scenario incurs substantial development and deployment costs. To address this issue, we introduce a multi-mode joiner that enables seamless integration of various ASR modes within a single unified model. Experiments show that All-in-One ASR significantly reduces the total model footprint while matching or even surpassing the recognition performance of individually optimized ASR models. Furthermore, joint decoding leverages the complementary strengths of different ASR modes, yielding additional improvements in recognition accuracy.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents a novel framework that unifies multiple ASR paradigms into a single model, significantly reducing complexity and enhancing performance. The comprehensive methodology and rigorous experimental validation highlight its potential to advance the state of the art in automatic speech recognition.
Methodology
The proposed All-in-One ASR framework introduces a multi-mode joiner that effectively integrates CTC, AED, and Transducer models into a single architecture. This unification is significant as it reduces the model footprint and computational overhead while maintaining or improving recognition performance. The methodology is well-structured, leveraging joint training and decoding strategies to exploit the strengths of different ASR paradigms without the need for separate decoder branches. The use of a shared encoder and the innovative joiner mechanism are noteworthy contributions that address the challenges of model complexity and resource efficiency in ASR systems.
Results & evaluation
The experimental evaluation is robust, utilizing well-established datasets such as TED-LIUM and LibriSpeech to demonstrate the effectiveness of the All-in-One ASR framework. The results indicate that the proposed model not only matches but often surpasses the performance of individually optimized models across various ASR tasks. The paper provides detailed comparisons and ablation studies that substantiate the claims of improved performance and reduced model size, showcasing the framework's versatility in both offline and streaming modes.
Reproducibility
While the paper outlines the architecture and training procedures in detail, it lacks specific URLs or repositories for code and datasets, which could hinder reproducibility. The absence of a public demo or project page further limits the ability of other researchers to replicate the results. However, the comprehensive description of the methodologies and experimental setups provides a solid foundation for future implementations.
Limitations
One limitation is the potential complexity introduced by the multi-mode joiner, which may require careful tuning of hyperparameters to achieve optimal performance across different ASR tasks. Additionally, the paper does not address the implications of scaling this framework to more complex or diverse ASR tasks beyond those tested. The reliance on specific datasets may also limit the generalizability of the findings.
Broader impact
The All-in-One ASR framework has significant implications for the deployment of ASR systems in resource-constrained environments, such as mobile devices or embedded systems, where model size and computational efficiency are critical. By unifying multiple ASR paradigms, this approach could streamline the development process and reduce costs, making advanced speech recognition technology more accessible across various applications. The paper presents a novel framework that unifies multiple ASR paradigms into a single model, significantly reducing complexity and enhancing performance. The comprehensive methodology and rigorous experimental validation highlight its potential to advance the state of the art in automatic speech recognition.
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