Results of the summarizing process for the arXiv paper: 2109.01164v1

The paper "Scalable Data Annotation Pipeline for High-Quality Large Speech Datasets Development" introduces a human-in-the-loop (HITL) data annotation pipeline that combines the advantages of both humans and machines to generate high-quality, large-scale speech datasets. The pipeline uses machine pre-labeling and fully manual auditing to annotate datasets quickly, accurately, and cost-effectively. 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. The authors conducted A/B testing and pilot studies that demonstrated the HITL pipeline can improve annotation speed and capacity by at least 80%, while maintaining comparable or higher quality than manual double pass annotation. They are leveraging this scalable pipeline to create 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. The UHV-OTS is a long term Appen project that aims to support commercial and academic research data needs in speech processing. To support academic and open source community research under the CC BY SA license, Appen will donate a number of free speech datasets from the UHV OTS each year. Additionally, the authors are releasing the code of the data preprocessing and pre tagging pipeline under Apache 2.0 license to allow reproduction of their results. Overall, this paper presents an innovative approach for generating high quality speech datasets at scale using a combination of human expertise and machine learning techniques. The HITL pipeline has significant implications for advancing research in speech processing by providing researchers with access to large volumes of annotated data that can be used for training machine learning models.

• The paper introduces a human-in-the-loop (HITL) data annotation pipeline that combines the advantages of both humans and machines to generate high-quality, large-scale speech datasets.
• The pipeline uses machine pre-labeling and fully manual auditing to annotate datasets quickly, accurately, and cost-effectively.
• 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.
• A/B testing and pilot studies demonstrated the HITL pipeline can improve annotation speed and capacity by at least 80%, while maintaining comparable or higher quality than manual double pass annotation.
• The scalable pipeline is being used to create 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.
• Appen will donate a number of free speech datasets from the UHV OTS each year under CC BY SA license to support academic and open source community research.
• The authors are releasing the code of the data preprocessing and pre-tagging pipeline under Apache 2.0 license to allow reproduction of their results.

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

The advancement of speech processing research has been limited by the lack of access to high quality, large-scale datasets. To address this challenge, Appen has developed a human-in-the-loop (HITL) data annotation pipeline that combines the advantages of both humans and machines to generate high-quality, large-scale speech datasets. This paper presents an overview of the HITL pipeline and its implications for advancing research in speech processing.

Overview of HITL Pipeline

The HITL pipeline uses machine pre-labeling and fully manual auditing to annotate datasets quickly, accurately, and cost effectively. 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. The authors conducted A/B testing and pilot studies that demonstrated the HITL pipeline can improve annotation speed and capacity by at least 80%, while maintaining comparable or higher quality than manual double pass annotation.

UHV OTS Corpora

The authors are leveraging this scalable pipeline to create 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 UHV OTS corpora using dynamic packaging. The UHV OTS is a long term Appen project that aims to support commercial and academic research data needs in speech processing.

Supporting Academic Research

To support academic and open source community research under Creative Commons Attribution Share Alike license (CC BY SA), Appen will donate a number of free speech datasets from UHV OTS each year. Additionally, the authors are releasing code related to their work under Apache 2 license which allows reproduction of their results on Github repository: https://github/appenltd/scalable_data_annotation_pipeline .

Implications for Speech Processing Research

Overall, this paper presents an innovative approach for generating high quality speech datasets at scale using a combination of human expertise and machine learning techniques. The HITL pipeline has significant implications for advancing research in speech processing by providing researchers with access to large volumes of annotated data that can be used for training machine learning models more efficiently than ever before possible without sacrificing accuracy or quality standards set forth by industry professionals..