A Semi-Automatic Approach to Create Large Gender- and Age-Balanced Speaker Corpora: Usefulness of Speaker Diarization & Identification

TL;DR

This paper introduces a semi-automatic method for building large, balanced speaker corpora using speaker diarization and identification, significantly reducing manual effort and ensuring high-quality speech data across multiple demographic categories.

Contribution

The paper presents a novel semi-automatic pipeline combining speech detection, diarization, and human annotation to efficiently create balanced speaker corpora.

Findings

Automatic pipeline reduces manual processing by a factor of ten.

High-quality speech segments are obtained for most excerpts.

The method effectively balances speakers across age, gender, and recording periods.

Abstract

This paper presents a semi-automatic approach to create a diachronic corpus of voices balanced for speaker's age, gender, and recording period, according to 32 categories (2 genders, 4 age ranges and 4 recording periods). Corpora were selected at French National Institute of Audiovisual (INA) to obtain at least 30 speakers per category (a total of 960 speakers; only 874 have be found yet). For each speaker, speech excerpts were extracted from audiovisual documents using an automatic pipeline consisting of speech detection, background music and overlapped speech removal and speaker diarization, used to present clean speaker segments to human annotators identifying target speakers. This pipeline proved highly effective, cutting down manual processing by a factor of ten. Evaluation of the quality of the automatic processing and of the final output is provided. It shows the automatic processing compare to up-to-date process, and that the output provides high quality speech for most of the selected excerpts. This method shows promise for creating large corpora of known target speakers.