Target Speech Extraction: Independent Vector Extraction Guided by Supervised Speaker Identification

TL;DR

This paper introduces a robust method for extracting a target speaker from audio mixtures by combining independent vector extraction guided by deep learning-based speaker identification, with an iterative deflation process to improve accuracy in challenging scenarios.

Contribution

It presents a novel guided independent vector extraction method with an intrinsic non-intrusive quality check and iterative deflation, enhancing target speech extraction in complex acoustic environments.

Findings

Effective in challenging conditions like reverberation and noise

Outperforms state-of-the-art blind and supervised methods

Reduces incorrect extractions through iterative deflation

Abstract

This manuscript proposes a novel robust procedure for the extraction of a speaker of interest (SOI) from a mixture of audio sources. The estimation of the SOI is performed via independent vector extraction (IVE). Since the blind IVE cannot distinguish the target source by itself, it is guided towards the SOI via frame-wise speaker identification based on deep learning. Still, an incorrect speaker can be extracted due to guidance failings, especially when processing challenging data. To identify such cases, we propose a criterion for non-intrusively assessing the estimated speaker. It utilizes the same model as the speaker identification, so no additional training is required. When incorrect extraction is detected, we propose a ``deflation'' step in which the incorrect source is subtracted from the mixture and, subsequently, another attempt to extract the SOI is performed. The process is repeated until successful extraction is achieved. The proposed procedure is experimentally tested on artificial and real-world datasets containing challenging phenomena: source movements, reverberation, transient noise, or microphone failures. The method is compared with state-of-the-art blind algorithms as well as with current fully supervised deep learning-based methods.