Paper Info
Title
Progressive AutoSpeech: An Efficient and General Framework for Automatic Speech Classification
Abstract
Speech classification has been widely used in many speech-related applications. However, the complexity of speech classification tasks often exceeds the scope of non-experts, the off-the-shelf speech classification methods are urgently needed. Recently, the automatic speech classification (AutoSpeech) without any human intervention has attracted more and more attention. The practical AutoSpeech solution should be general and can automatically handle classification tasks from different domains. Moreover, AutoSpeech should improve not only the final performance but also the any-time performance especially when the time budget is limited. To address these issues, we propose a three-stage any-time learning algorithm framework called Progressive AutoSpeech for automatic speech classification under a given time budget. Progressive AutoSpeech consists of the fast stage, enhancement stage, and exploration stage. Each stage uses different models and features to ensure generalization. Additionally, we automatically construct ensembles of top-k prediction results to improve the robustness. The experimental results reveal that Progressive AutoSpeech is effective and efficient for a wide range of speech classification tasks and can achieve the best ALC score.
Year
DOI
Venue
2021
10.1007/978-3-030-75765-6_14
ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PAKDD 2021, PT II
Keywords
DocType
Volume
Automatic speech classification, Deep learning, Any-time learning
Conference
12713
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Guanghui Zhu100.34
Feng Cheng200.68
Mengchuan Qiu301.35
Zhuoer Xu400.68
Wenjie Wang500.34
Chunfeng Yuan656.90
Yihua Huang786.61