TITLE: CrowdSpeech and VoxDIY: Benchmark Datasets for Crowdsourced Audio Transcription 

ABSTRACT: Domain-specific data is the crux of the successful transfer of machine
learning systems from benchmarks to real life. In simple problems such as image
classification, crowdsourcing has become one of the standard tools for cheap
and time-efficient data collection: thanks in large part to advances in
research on aggregation methods. However, the applicability of crowdsourcing to
more complex tasks (e.g., speech recognition) remains limited due to the lack
of principled aggregation methods for these modalities. The main obstacle
towards designing aggregation methods for more advanced applications is the
absence of training data, and in this work, we focus on bridging this gap in
speech recognition. For this, we collect and release CrowdSpeech -- the first
publicly available large-scale dataset of crowdsourced audio transcriptions.
Evaluation of existing and novel aggregation methods on our data shows room for
improvement, suggesting that our work may entail the design of better
algorithms. At a higher level, we also contribute to the more general challenge
of developing the methodology for reliable data collection via crowdsourcing.
In that, we design a principled pipeline for constructing datasets of
crowdsourced audio transcriptions in any novel domain. We show its
applicability on an under-resourced language by constructing VoxDIY -- a
counterpart of CrowdSpeech for the Russian language. We also release the code
that allows a full replication of our data collection pipeline and share
various insights on best practices of data collection via crowdsourcing.
Domain-specific data is the crux of the successful transfer of machine
learning systems from benchmarks to real life. In simple problems such as image
classification, crowdsourcing has become one of the standard tools for cheap
and time-efficient data collection: thanks in large part to advances in
research on aggregation methods. However, the applicability of crowdsourcing to
more complex tasks (e.g., speech recognition) remains limited due to the lack
of principled aggregation methods for these modalities. The main obstacle
towards designing aggregation methods for more advanced applications is the
absence of training data, and in this work, we focus on bridging this gap in
speech recognition. For this, we collect and release CrowdSpeech -- the first
publicly available large-scale dataset of crowdsourced audio transcriptions.
Evaluation of existing and novel aggregation methods on our data shows room for
improvement, suggesting that our work may entail the design of better
algorithms. At a higher level, we also contribute to the more general challenge
of developing the methodology for reliable data collection via crowdsourcing.
In that, we design a principled pipeline for constructing datasets of
crowdsourced audio transcriptions in any novel domain. We show its
applicability on an under-resourced language by constructing VoxDIY -- a
counterpart of CrowdSpeech for the Russian language. We also release the code
that allows a full replication of our data collection pipeline and share
various insights on best practices of data collection via crowdsourcing.
CrowdSpeech and VoxDIY: Benchmark Datasets for Crowdsourced Audio Transcription
http://arxiv.org/abs/2107.01091v2