Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: Towards Building Voice-based Conversational Recommender Systems: Datasets, Potential Solutions, and Prospects
summary: Conversational recommender systems (CRSs) have become crucial emerging
research topics in the field of RSs, thanks to their natural advantages of
explicitly acquiring user preferences via interactive conversations and
revealing the reasons behind recommendations. However, the majority of current
CRSs are text-based, which is less user-friendly and may pose challenges for
certain users, such as those with visual impairments or limited writing and
reading abilities. Therefore, for the first time, this paper investigates the
potential of voice-based CRS (VCRSs) to revolutionize the way users interact
with RSs in a natural, intuitive, convenient, and accessible fashion. To
support such studies, we create two VCRSs benchmark datasets in the e-commerce
and movie domains, after realizing the lack of such datasets through an
exhaustive literature review. Specifically, we first empirically verify the
benefits and necessity of creating such datasets. Thereafter, we convert the
user-item interactions to text-based conversations through the ChatGPT-driven
prompts for generating diverse and natural templates, and then synthesize the
corresponding audios via the text-to-speech model. Meanwhile, a number of
strategies are delicately designed to ensure the naturalness and high quality
of voice conversations. On this basis, we further explore the potential
solutions and point out possible directions to build end-to-end VCRSs by
seamlessly extracting and integrating voice-based inputs, thus delivering
performance-enhanced, self-explainable, and user-friendly VCRSs. Our study aims
to establish the foundation and motivate further pioneering research in the
emerging field of VCRSs. This aligns with the principles of explainable AI and
AI for social good, viz., utilizing technology's potential to create a fair,
sustainable, and just world.
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Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: Towards Building Voice-based Conversational Recommender Systems: Datasets, Potential Solutions, and Prospects
summary: Conversational recommender systems (CRSs) have become crucial emerging research topics in the field of RSs, thanks to their natural advantages of explicitly acquiring user preferences via interactive conversations and revealing the reasons behind recommendations. However, the majority of current CRSs are text-based, which is less user-friendly and may pose challenges for certain users, such as those with visual impairments or limited writing and reading abilities. Therefore, for the first time, this paper investigates the potential of voice-based CRS (VCRSs) to revolutionize the way users interact with RSs in a natural, intuitive, convenient, and accessible fashion. To support such studies, we create two VCRSs benchmark datasets in the e-commerce and movie domains, after realizing the lack of such datasets through an exhaustive literature review. Specifically, we first empirically verify the benefits and necessity of creating such datasets. Thereafter, we convert the user-item interactions to text-based conversations through the ChatGPT-driven prompts for generating diverse and natural templates, and then synthesize the corresponding audios via the text-to-speech model. Meanwhile, a number of strategies are delicately designed to ensure the naturalness and high quality of voice conversations. On this basis, we further explore the potential solutions and point out possible directions to build end-to-end VCRSs by seamlessly extracting and integrating voice-based inputs, thus delivering performance-enhanced, self-explainable, and user-friendly VCRSs. Our study aims to establish the foundation and motivate further pioneering research in the emerging field of VCRSs. This aligns with the principles of explainable AI and AI for social good, viz., utilizing technology's potential to create a fair, sustainable, and just world.
id: http://arxiv.org/abs/2306.08219v1
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