Data-driven Speech Enhancement: from Non-negative Matrix Factorization to Deep Representation Learning (2023)
Abstract / truncated to 115 words
In natural listening environments, speech signals are easily distorted by variousacoustic interference, which reduces the speech quality and intelligibility of human listening; meanwhile, it makes difficult for many speech-related applications, such as automatic speech recognition (ASR). Thus, many speech enhancement (SE) algorithms have been developed in the past decades. However, most current SE algorithms are difficult to capture underlying speech information (e.g., phoneme) in the SE process. This causes it to be challenging to know what specific information is lost or interfered with in the SE process, which limits the application of enhanced speech. For instance, some SE algorithms aimed to improve human listening usually damage the ASR system. The objective of this dissertation is ... toggle 3 keywordsspeech enhancement – non-negative matrix factorization – deep representation learning
- Xiang, Yang
- Aalborg University, Capturi A/S
- Publication Year
- Upload Date
- Feb. 14, 2023
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