Work

Real-Time Speech Extraction Using Spatially Regularized Independent Low-Rank Matrix Analysis and Rank-Constrained Spatial Covariance Matrix Estimation

Year: 2024

Type: conference-paper

Abstract: Real-time speech extraction is an important challenge with various applications such as speech recognition in a human-like avatar/robot. In this paper, we propose the real-time extension of a speech extraction method based on independent low-rank matrix analysis (ILRMA) and rank-constrained spatial covariance matrix estimation (RCSCME). The RCSCME-based method is a multichannel blind speech extraction method that demonstrates superior speech extraction performance in diffuse noise environments. To improve the performance, we introduce spatial regularization into the ILRMA part of the RCSCME-based speech extraction and design two regularizers. Speech extraction experiments demonstrated that the proposed methods can function in real time and the designed regularizers improve the speech extraction performance.

Source: IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)

DOI: 10.1109/icasspw62465.2024.10627448

Authors: Yuto Ishikawa, Kohei Konaka, Tomohiko Nakamura, Norihiro Takamune, Hiroshi Saruwatari

Institutions: The University of Tokyo, National Institute of Advanced Industrial Science and Technology

Language: English

Topic: Speech and Audio Processing

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Tateisi Science and Technology Foundation, Moonshot Research and Development Program

Award: G556986091