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The performances of automatic speech recognition (ASR) systems degradedrastically under noisy conditions. Explicit distortion modelling (EDM), as afeature compensation step, is able to enhance ASR systems under such conditionsby simulating the in-domain noisy speeches from the clean counterparts. Yet,existing distortion models are either non-trainable or unexplainable and oftenlack controllability and generalization ability. In this paper, we propose afully explainable and controllable model: DENT-DDSP to achieve EDM. DENT-DDSPutilizes novel differentiable digital signal processing (DDSP) components andrequires only 10 seconds of training data to achieve high fidelity. Theexperiment shows that the simulated noisy data from DENT-DDSP achieves thehighest simulation fidelity compared to other baseline models in terms ofmulti-scale spectral loss (MSSL). Moreover, to validate whether the datasimulated by DENT-DDSP are able to replace the scarce in-domain noisy data inthe noise-robust ASR tasks, several downstream ASR models with the samearchitecture are trained using the simulated data and the real data. Theexperiment shows that the model trained with the simulated noisy data fromDENT-DDSP achieves similar performances to the benchmark with a 2.7\%difference in terms of word error rate (WER). The code of the model is releasedonline.

DENT-DDSP: Data-efficient noisy speech generator using differentiable  digital signal processors for explicit distortion modelling and noise-robust  speech recognition