Summary of Three-in-one: Fast and Accurate Transducer For Hybrid-autoregressive Asr, by Hainan Xu et al.
Three-in-One: Fast and Accurate Transducer for Hybrid-Autoregressive ASR
by Hainan Xu, Travis M. Bartley, Vladimir Bataev, Boris Ginsburg
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes Hybrid-Autoregressive INference TrANsducers (HAINAN), a novel architecture for speech recognition that builds upon the Token-and-Duration Transducer (TDT) model. The HAINAN architecture is trained with randomly masked predictor network outputs, allowing it to support both autoregressive and non-autoregressive inference modes. Additionally, the paper introduces a semi-autoregressive inference paradigm that generates an initial hypothesis using non-autoregressive inference and then refines it through parallelized autoregression. The authors demonstrate the effectiveness of HAINAN on multiple datasets across different languages, achieving efficiency parity with CTC in non-autoregressive mode and outperforming TDT and RNN-T in autoregressive mode. Semi-autoregressive inference further enhances the model’s accuracy with minimal computational overhead. Overall, the paper highlights HAINAN’s flexibility in balancing accuracy and speed, making it a strong candidate for real-world speech recognition applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HAINAN is a new way to recognize speech that combines different ideas from previous research. The authors trained their model using something called “masked predictor network outputs.” This allows the model to make predictions about what comes next in an audio clip, either by looking at what has come before (autoregressive) or without looking at what came before (non-autoregressive). They also proposed a new way of combining these two approaches, which they call “semi-autoregressive inference.” The authors tested their model on several datasets and found that it was better than other models at recognizing speech. This is important because HAINAN can be used in real-world applications like voice assistants or speech-to-text systems. |
Keywords
» Artificial intelligence » Autoregressive » Inference » Rnn » Token
