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Updated: Oct 30, 2025

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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Multi-microphone Complex Spectral Mapping for Utterance-wise and Continuous Speech Separation.

Zhong-Qiu Wang1, Peidong Wang2, DeLiang Wang3

  • 1Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210-1277 USA, while performing this work. He is now with Mitsubishi Electric Research Laboratories, Cambridge, MA 02139, USA.

IEEE/ACM Transactions on Audio, Speech, and Language Processing
|July 2, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces deep learning for speaker separation and dereverberation using multi-microphone complex spectral mapping. The method effectively separates speech in noisy, reverberant environments, achieving state-of-the-art performance.

Keywords:
Complex spectral mappingdeep learningmicrophone array processingspeaker separation

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Area of Science:

  • Signal Processing
  • Machine Learning
  • Acoustics

Background:

  • Speaker separation and dereverberation are crucial for clear audio in real-world environments.
  • Traditional methods struggle with non-linear distortions and reverberation in multi-microphone settings.

Purpose of the Study:

  • To develop a deep learning-based approach for time-varying non-linear beamforming for speaker separation.
  • To achieve both speaker separation and dereverberation in reverberant conditions.
  • To extend the method from offline to block-online continuous speech separation.

Main Methods:

  • Proposed multi-microphone complex spectral mapping using deep neural networks (DNNs).
  • DNNs predict real and imaginary components of target speech from multi-microphone inputs.
  • Integrated with Minimum Variance Distortionless Response (MVDR) beamforming and post-filtering for enhanced separation.
  • Utilized frame-level speaker counting for block-online continuous speech separation (CSS).

Main Results:

  • The proposed system generalizes well from simulated to real microphone array data with fixed geometry.
  • Achieved state-of-the-art speaker separation performance on simulated (SMS-WSJ) and real (LibriCSS) datasets.
  • Demonstrated effectiveness in both utterance-wise offline and block-online continuous speech separation.

Conclusions:

  • Multi-microphone complex spectral mapping offers a simple yet effective deep learning solution for challenging speech separation tasks.
  • The method successfully addresses reverberation and non-linearities, improving audio clarity.
  • The approach is robust and performs well on both simulated and real-world acoustic data.