Papers

Gradient-based active learning query strategy for end-to-end speech recognition

International Conference
2016~2020
작성자
한혜원
작성일
2019-05-01 16:40
조회
214
Authors : Yang Yuan, Soo-Whan Chung, Hong-Goo Kang

Year : 2019

Publisher / Conference : ICASSP

In this paper, we propose an effective active learning query strategy for an automatic speech recognition system with the aim of reducing the training cost. Generally, training a deep neural network with supervised learning requires a massive amount of labeled data to obtain excellent performance. However, labeling data is tedious and costly manual work. Active learning can solve this problem by choosing and only annotating informative instances, which presents better results even with less transcribed data. In this approach it is vitally important to accurately select informative samples. Based on the preliminary experiment results that true gradient length has the best performance in terms of measuring sample informativeness in ideal conditions, we propose utilizing both uncertainty and the expected gradient length criterion to approximate the true gradient length using a neural network. The experiment results show that our proposed method is superior to the conventional individual criterion when applied to a phoneme-based speech recognition system, and it has both a faster convergence speed and the greatest loss reduction in both clean and noisy conditions.
전체 322
282 International Conference Yang Yuan, Soo-Whan Chung, Hong-Goo Kang "Gradient-based active learning query strategy for end-to-end speech recognition" in ICASSP, 2019
281 International Conference Soo-Whan Chung, Joon Son Chung, Hong-Goo Kang "Perfect match: Improved cross-modal embeddings for audio-visual synchronisation" in ICASSP, 2019
280 International Conference Hyewon Han, Kyungguen Byun, Hong-Goo Kang "A Deep Learning-based Stress Detection Algorithm with Speech Signal" in Workshop on Audio-Visual Scene Understanding for Immersive Multimedia (AVSU’18), 2018
279 International Conference Min-Jae Hwang, Eunwoo Song, Jin-Seob Kim, Hong-Goo Kang "A Unified Framework for the Generation of Glottal Signals in Deep Learning-based Parametric Speech Synthesis Systems" in INTERSPEECH, 2018
278 Domestic Conference 양원, 정수환, 강홍구 "비학습 데이터 적응화 기법을 이용한 딥러닝 기반 한국어 음성 인식 기술" in 한국음향학회 추계발표대회, 2018
277 Domestic Conference 최소연, 정수환, 강홍구 "임베딩 매트릭스를 기반으로 한 비정상적 잡음 제거 알고리즘의 분석과 딥러닝 음질개선 방법들과의 성능비교" in 한국음향학회 추계발표대회, 2018
276 International Journal Jinkyu Lee, Jan Skoglund, Turaj Shabestary, Hong-Goo Kang "Phase-Sensitive Joint Learning Algorithms for Deep Learning-Based Speech Enhancement" in IEEE Signal Processing Letters, vol.25, issue 8, pp.1276-1280, 2018
275 International Conference Haemin Yang, Soyeon Choe, Keulbit Kim, Hong-Goo Kang "Deep learning-based speech presence probability estimation for noise PSD estimation in single-channel speech enhancement" in ICSigSys, 2018
274 International Conference Min-Jae Hwang, Eunwoo Song, Kyungguen Byun, Hong-Goo Kang "Modeling-by-Generation-Structured Noise Compensation Algorithm for Glottal Vocoding Speech Synthesis System" in ICASSP, 2018
273 International Conference Jinyoung Lee, Chahyeon Eom, Youngsu Kwak, Hong-Goo Kang, Chungyoung Lee "DNN-based Wireless Positioning in An Outdoor Environment" in ICASSP, 2018