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Abstract
In this paper, we present the Blind Speech Separation and Dereverberation (BSSD) network, which performs simultaneous speaker separation, dereverberation and speaker identification in a single neural network. Speaker separation is guided by a set of predefined spatial cues. Dereverberation is performed by using neural beamforming, and speaker identification is aided by embedding vectors and triplet mining. We introduce a frequency-domain model which uses complex-valued neural networks, and a time-domain variant which performs beamforming in latent space. Further, we propose a block-online mode to process longer audio recordings, as they occur in meeting scenarios. We evaluate our system in terms of Scale Independent Signal to Distortion Ratio (SI-SDR), Word Error Rate (WER) and Equal Error Rate (EER).
Originalsprache | englisch |
---|---|
Seiten (von - bis) | 29-41 |
Seitenumfang | 13 |
Fachzeitschrift | Speech Communication |
Jahrgang | 140 |
DOIs | |
Publikationsstatus | Veröffentlicht - Mai 2022 |
ASJC Scopus subject areas
- Software
- Kommunikation
- Sprache und Linguistik
- Maschinelles Sehen und Mustererkennung
- Angewandte Informatik
- Modellierung und Simulation
- Linguistik und Sprache
Fields of Expertise
- Information, Communication & Computing
Projekte
- 1 Laufend