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Prosody is closely linked to the identity of a speaker, leading to individual pitch and intonation patterns. Therefore, it is challenging in speaker anonymization to generate speech utterances that both keep the original audio’s main prosodic structure and preserve the speaker’s privacy. In this paper, we present a system that extends a speech-to-text-to-speech anonymization pipeline with prosody cloning and show how to control the cloning by multiplying pitch and energy sequences with random offset values. Using automatic and human evaluation, we find this combination to successfully overcome the privacy-utility trade-off for prosody by achieving high privacy and high pitch correlation scores. At the same time, the anonymized utterances prove to reproduce the original voice distinctiveness and content with high intelligibility and only a small loss in naturalness, making them suitable for downstream applications.

Customizing voice and speaking style in a speech synthesis system with intuitive and fine-grained controls is challenging, given that little data with appropriate labels is available. Furthermore, editing an existing human's voice also comes with ethical concerns. In this paper, we propose a method to generate artificial speaker embeddings that cannot be linked to a real human while offering intuitive and fine-grained control over the voice and speaking style of the embeddings, without requiring any labels for speaker or style. The artificial and controllable embeddings can be fed to a speech synthesis system, conditioned on embeddings of real humans during training, without sacrificing privacy during inference.

Since the end of the last century, the automatic processing of paralinguistics has been investigated widely and put into practice in many applications, on wearables, smartphones, and computers. In this contribution, we address ethical awareness for paralinguistic applications, by establishing taxonomies for data representations, system designs for and a typology of applications, and users/test sets and subject areas. These are related to an “ethical grid” consisting of the most relevant ethical cornerstones, based on principalism. The characteristics of and the interdependencies between these taxonomies are described and exemplified. This makes it possible to assess more or less critical “ethical constellations.” To the best of our knowledge, this is the first attempt of its kind.

In order to protect the privacy of speech data, speaker anonymization aims for hiding the identity of a speaker by changing the voice in speech recordings. This typically comes with a privacy-utility trade-off between protection of individuals and usability of the data for downstream applications. One of the challenges in this context is to create non-existent voices that sound as natural as possible. In this work, we propose to tackle this issue by generating speaker embeddings using a generative adversarial network with Wasserstein distance as cost function. By incorporating these artificial embeddings into a speech-to-text-to-speech pipeline, we outperform previous approaches in terms of privacy and utility. According to standard objective metrics and human evaluation, our approach generates intelligible and content-preserving yet privacy-protecting versions of the original recordings.

Previous research has found that task-oriented conversational agents are perceived more positively by users when they provide information in an empathetic manner compared to a plain, emotionless information exchange.However, users' perception and ethical considerations related to a dialog systems' response language style have received comparatively little attention in the field of human-computer interaction.To bridge this gap, we explored these ethical implications through a scenario-based user study.127 participants interacted with one of three variants of an affective, task-oriented conversational agent, each variant providing responses in a different language style.After the interaction, participants filled out a survey about their feelings during the experiment and their perception of various aspects of the chatbot.Based on statistical and qualitative analysis of the responses, we found language style played an important role in how humanlike participants perceived a dialog agent as well as how likable.Language style also had a direct effect on how users perceived the use of personal pronouns 'I' and 'You' and how they projected gender onto the chatbot.Finally, we identify and discuss ethical implications.In particular we focus on what factors/stereotypes influenced participants' impressions of gender, and what trade-offs a more human-like chatbot brings.