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

International Conference
2019-05-01 16:40
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 355
315 International Conference Kihyuk Jeong, Huu-Kim Nguyen, Hong-Goo Kang "A Fast and Lightweight Text-To-Speech Model with Spectrum and Waveform Alignment Algorithms" in EUSIPCO, 2021
314 International Conference Jiyoung Lee*, Soo-Whan Chung*, Sunok Kim, Hong-Goo Kang**, Kwanghoon Sohn** "Looking into Your Speech: Learning Cross-modal Affinity for Audio-visual Speech Separation" in CVPR, 2021
313 International Conference Zainab Alhakeem, Hong-Goo Kang "Confidence Learning from Noisy Labels for Arabic Dialect Identification" in ITC-CSCC, 2021
312 International Conference Huu-Kim Nguyen, Kihyuk Jeong, Hong-Goo Kang "Fast and Lightweight Speech Synthesis Model based on FastSpeech2" in ITC-CSCC, 2021
311 International Conference Yoohwan Kwon*, Hee-Soo Heo*, Bong-Jin Lee, Joon Son Chung "The ins and outs of speaker recognition: lessons from VoxSRC 2020" in ICASSP, 2021
310 International Conference You Jin Kim, Hee Soo Heo, Soo-Whan Chung, Bong-Jin Lee "End-to-end Lip Synchronisation Based on Pattern Classification" in IEEE Spoken Language Technology Workshop (SLT), 2020
309 International Conference Seong Min Kye, Yoohwan Kwon, Joon Son Chung "Cross Attentive Pooling for Speaker Verification" in IEEE Spoken Language Technology Workshop (SLT), 2020
308 International Conference Suhyeon Oh, Hyungseob Lim, Kyungguen Byun, Min-Jae Hwang, Eunwoo Song, Hong-Goo Kang "ExcitGlow: Improving a WaveGlow-based Neural Vocoder with Linear Prediction Analysis" in APSIPA (*awarded Best Paper), 2020
307 International Conference Hyeon-Kyeong Shin, Hyewon Han, Kyungguen Byun, Hong-Goo Kang "Speaker-invariant Psychological Stress Detection Using Attention-based Network" in APSIPA, 2020
306 International Conference Min-Jae Hwang, Frank Soong, Eunwoo Song, Xi Wang, Hyeonjoo Kang, Hong-Goo Kang "LP-WaveNet: Linear Prediction-based WaveNet Speech Synthesis" in APSIPA, 2020