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Found 6 papers, 0 papers with code
Intermediate-layer output Regularization for Attention-based Speech Recognition with Shared Decoder
Intermediate layer output (ILO) regularization by means of multitask training on encoder side has been shown to be an effective approach to yielding improved results on a wide range of end-to-end ASR frameworks.
Internal Language Model Estimation based Language Model Fusion for Cross-Domain Code-Switching Speech Recognition
More importantly, we train an end-to-end (E2E) speech recognition model by means of merging two monolingual data sets and observe the efficacy of the proposed ILME-based LM fusion for CSSR.
Minimum word error training for non-autoregressive Transformer-based code-switching ASR
Non-autoregressive end-to-end ASR framework might be potentially appropriate for code-switching recognition task thanks to its inherent property that present output token being independent of historical ones.
E2E-based Multi-task Learning Approach to Joint Speech and Accent Recognition
In this paper, we propose a single multi-task learning framework to perform End-to-End (E2E) speech recognition (ASR) and accent recognition (AR) simultaneously.
SN 2017hpa: A Nearby Carbon-Rich Type Ia Supernova with a Large Velocity Gradient
We present extensive, well-sampled optical and ultraviolet photometry and optical spectra of the Type Ia supernova (SN Ia) 2017hpa.
High Energy Astrophysical Phenomena Solar and Stellar Astrophysics
Multilingual Approach to Joint Speech and Accent Recognition with DNN-HMM Framework
Experimental results on an 8-accent English speech recognition show both methods can yield WERs close to the conventional ASR systems that completely ignore the accent, as well as desired AR accuracy.
