Papers

효율적인 양자화 기법을 통한 DNN 기반 화자 인식 모델 최적화

Domestic Conference
작성자
dsp
작성일
2024-07-09 13:31
조회
68
Authors : Yeona Hong, Woo-Jin Chung, Hong-Goo Kang

Year : 2024

Publisher / Conference : 대한전자공학회 2024년도 하계종합학술대회

Research area : Speech Signal Processing, Etc

Presentation : Oral

As Deep Neural Networks (DNNs) rapidly advance in various fields, including speech verification, they typically involve high computational costs and substantial memory consumption, which can be challenging to manage on mobile systems. Quantization of deep models offers a means to reduce both computational and memory expenses. Our research proposes an optimization framework for the quantization of the speaker verification model. By analyzing performance changes and model size reductions in each layer of a pre-trained speaker verification model, we have effectively minimized performance degradation while significantly reducing the model size. Our quantization algorithm is the first attempt to maintain the performance of the state-of-the-art pre-trained speaker verification model, ECAPATDNN, while significantly compressing its model size. Overall, our quantization approach resulted in reducing the model size by half, with an increase in EER limited to 0.07%.
전체 364
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363 Domestic Conference Yeona Hong, Woo-Jin Chung, Hong-Goo Kang "효율적인 양자화 기법을 통한 DNN 기반 화자 인식 모델 최적화" in 대한전자공학회 2024년도 하계종합학술대회, 2024
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357 International Conference Juhwan Yoon, Woo Seok Ko, Seyun Um, Sungwoong Hwang, Soojoong Hwang, Changhwan Kim, Hong-Goo Kang "UNIQUE : Unsupervised Network for Integrated Speech Quality Evaluation" in INTERSPEECH, 2024
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355 International Conference Hyewon Han, Naveen Kumar "A cross-talk robust multichannel VAD model for multiparty agent interactions trained using synthetic re-recordings" in Hands-free Speech Communication and Microphone Arrays (HSCMA, Satellite workshop in ICASSP), 2024