Scalable Data Annotation Pipeline for High-Quality Large Speech Datasets Development

Judith Bishop, Mingkuan Liu, Monchu Chen, Chao Feng, Chi Zhang, Hua Xing, Grace Ngapo

Abstract:

This paper introduces a human-in-the-loop (HITL) data annotation pipeline to
generate high-quality, large-scale speech datasets. The pipeline combines human
and machine advantages to more quickly, accurately, and cost-effectively annotate
datasets with machine pre-labeling and fully manual auditing. Quality control
mechanisms such as blind testing, behavior monitoring, and data validation have
been adopted in the annotation pipeline to mitigate potential bias introduced by
machine-generated labels. Our A/B testing and pilot results demonstrated the HITL
pipeline can improve annotation speed and capacity by at least 80% and quality is
comparable to or higher than manual double pass annotation. We are leveraging this
scalable pipeline to create and continuously grow ultra-high volume off-the-shelf
(UHV-OTS) speech corpora for multiple languages, with the capability to expand
to 10,000+ hours per language annually. Customized datasets can be produced
from the UHV-OTS corpora using dynamic packaging. UHV-OTS is a long-term
Appen project to support commercial and academic research data needs in speech
processing. Appen will donate a number of free speech datasets from the UHV-
OTS each year to support academic and open source community research under
the CC-BY-SA license. We are also releasing the code of the data pre-processing
and pre-tagging pipeline under the Apache 2.0 license to allow reproduction of the
results reported in the paper.