Objective quality assessment of wideband speech by an extension of ITU-t recommendation p.862

Takahashi, Akihiro; Kurashima, Atsuko; Morioka, Chiharu; Yoshino, Hideaki

doi:10.21437/interspeech.2005-839

Cited by 5 publications

(1 citation statement)

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“…PKT [12] uses cosine similarity to compress the intermediate representation of the model, which is similar to the idea of this paper, so it is also used for comparison. Wideband PESQ (WB-PESQ) [23] and STOI [24] are used as objective indicators for speech quality assessment.…”

Section: Resultsmentioning

confidence: 99%

Cross-Layer Similarity Knowledge Distillation for Speech Enhancement

Cheng¹,

Liang²,

Xie³

et al. 2022

Interspeech 2022

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Speech enhancement (SE) algorithms based on deep neural networks (DNNs) often encounter challenges of limited hardware resources or strict latency requirements when deployed in realworld scenarios. However, a strong enhancement effect typically requires a large DNN. In this paper, a knowledge distillation framework for SE is proposed to compress the DNN model. We study the strategy of cross-layer connection paths, which fuses multi-level information from the teacher and transfers it to the student. To adapt to the SE task, we propose a frame-level similarity distillation loss. We apply this method to the deep complex convolution recurrent network (DCCRN) and make targeted adjustments. Experimental results show that the proposed method considerably improves the enhancement effect of the compressed DNN and outperforms other distillation methods.

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Section: Resultsmentioning

confidence: 99%