Three-in-One: Fast and Accurate Transducer for Hybrid-Autoregressive ASR

We present Hybrid-Autoregressive INference TrANsducers (HAINAN), a novel architecture for speech recognition that extends the Token-and-Duration Transducer (TDT) model. Trained with randomly masked predictor network outputs, HAINAN supports both autoregressive inference with all network components and non-autoregressive inference without the predictor. Additionally, we propose a novel semi-autoregressive inference paradigm that first generates an initial hypothesis using non-autoregressive inference, followed by refinement steps where each token prediction is regenerated using parallelized autoregression on the initial hypothesis. Experiments on multiple datasets across different languages demonstrate that HAINAN achieves efficiency parity with CTC in non-autoregressive mode and with TDT in autoregressive mode. In terms of accuracy, autoregressive HAINAN outperforms TDT and RNN-T, while non-autoregressive HAINAN significantly outperforms CTC. Semi-autoregressive inference further enhances the model's accuracy with minimal computational overhead, and even outperforms TDT results in some cases. End-to-end neural automatic speech recognition (ASR) has seen significant advancements in recent years, namely due to the development of three architecture paradigms: Connectionist Temporal Classification (CTC) (Graves et al., 2006), Recurrent Neural Network Transducers (RNN-T) (Graves, 2012), and Attention-based Encoder and Decoder Models (Chorowski et al., 2015; Chan et al., 2016). These models have gained widespread adoption, supported by open-source projects such as ESPNet (Watanabe et al., 2018), SpeechBrain (Ravanelli et al., 2021), and NeMo (Kuchaiev et al., 2019), etc. CTC and RNN-T models share a frame-synchronous design, enabling streaming processing of speech input.