Discriminative Learning For Monaural Speech Separation Using Deep Embedding Features

Abstract

Deep clustering (DC) and utterance-level permutation invariant training (uPIT) have been demonstrated promising for speaker-independent speech separation. DC is usually formulated as two-step processes: embedding learning and embedding clustering, which results in complex separation pipelines and a huge obstacle in directly optimizing the actual separation objectives. As for uPIT, it only minimizes the chosen permutation with the lowest mean square error, doesn't discriminate it with other permutations. In this paper, we propose a discriminative learning method for speaker-independent speech separation using deep embedding features. Firstly, a DC network is trained to extract deep embedding features, which contain each source's information and have an advantage in discriminating each target speakers. Then these features are used as the input for uPIT to directly separate the different sources. Finally, uPIT and DC are jointly trained, which directly optimizes the actual separation obje

Stats

• citations13
• S2 citations—
• github stars0
• HF likes0
• heat score8.60
• arxiv keyfan2019discriminative

Related papers

• Multi-channel Speech Separation Using Deep Embedding Model With Multilayer Bootstrap Networks (2019)0.00
• Single-channel Multi-speaker Separation Using Deep Clustering (2016)0.00
• Orthonormal Embedding-based Deep Clustering For Single-channel Speech Separation (2019)0.00
• Spatial And Spectral Deep Attention Fusion For Multi-channel Speech Separation Using Deep Embedding Features (2020)0.00
• Permutation Invariant Training Of Deep Models For Speaker-independent Multi-talker Speech Separation (2016)0.00
• Multi-talker Speech Separation With Utterance-level Permutation Invariant Training Of Deep Recurrent Neural Networks (2017)20.90
• Single-channel Speech Separation Using Soft-minimum Permutation Invariant Training (2021)2.26
• Deep Attractor Network For Single-microphone Speaker Separation (2016)17.88