Evaluating speech synthesis intelligibility using Amazon Mechanical Turk

Microtask platforms such as Amazon Mechanical Turk (AMT) are increasingly used to create speech and language resources. AMT in particular allows researchers to quickly recruit a large number of fairly demographically diverse participants. In this study, we investigated whether AMT can be used for comparing the intelligibility of speech synthesis systems. We conducted two experiments in the lab and via AMT, one comparing US English diphone to US English speaker-adaptive HTS synthesis and one comparing UK English unit selection to UK English speaker-dependent HTS synthesis. While AMT word error rates were worse than lab error rates, AMT results were more sensitive to relative differences between systems. This is mainly due to the larger number of listeners. Boxplots and multilevel modelling allowed us to identify listeners who performed particularly badly, while thresholding was sufficient to eliminate rogue workers. We conclude that AMT is a viable platform for synthetic speech intelligibility comparisons

Twitter has a Binary Privacy Setting, are Users Aware of How It Works?

Twitter accounts are public by default, but Twitter gives the option to create protected accounts, where only approved followers can see their tweets. The publicly visible information changes based on the account type and the visibility of tweets also depends solely on the poster's account type which can cause unintended disclosures especially when users interact. We surveyed 336 Twitter users to understand users' awareness of account information visibility, as well as the tweet visibility when users interact. We find that our participants are aware of the visibility of their profile information and individual tweets. However, the visibility of followed topics, lists, and interactions with protected accounts is confusing. Only 31% of the participants were aware that a reply by a public account to a protected account's tweet would be publicly visible. Surprisingly, having a protected account does not result in a better understanding of the account information or tweet visibility.Comment: Proceeding of the 2023 ACM SIGCHI Conference on Computer-Supported Cooperative Work & Social Computing (CSCW'23

Prosody and the Resolution of Pronominal Anaphora

In this paper, we investigate the acoustic prosodic marking of demonstrative and personal pronouns in taskoriented dialog. Although it has been hypothesized that acoustic marking affects pronoun resolution, we find that the prosodic information extracted from the data is not sufficient to predict antecedent type reliably. Interspeaker variation accounts for much of the prosodic variation that we find in our data. We conclude that prosodic cues should be handled with care in robust, speakerindependent dialog systems. 1 Introduction Previous work on anaphora resolution has yielded a rich basis of theories and heuristics for finding antecedents. However, most research to date has neglected an important potential cue that is only available in spoken data: prosody. Prosodic marking can be used to change the antecedent of a pronoun, as demonstrated by this classic example from Lakoff (1971) (capitals indicate a pitch accent): (1) John i called Jim j a Republican, then he i insulted him j ..

The miscarriage circle of care:towards leveraging online spaces for social support

BACKGROUND: Lack of social support during and after miscarriage can greatly affect mental wellbeing. With miscarriages being a common experience, there remains a discrepancy in the social support received after a pregnancy is lost. METHOD: 42 people who had experienced at least one miscarriage took part in an Asynchronous Remote Community (ARC) study. The study involved 16 activities (discussions, creative tasks, and surveys) in two closed, secret Facebook groups over eight weeks. Descriptive statistics were used to analyse quantitative data, and content analysis was used for qualitative data. RESULTS: There were two main miscarriage care networks, formal (health care providers) and informal (friends, family, work colleagues). The formal care network was the most trusted informational support source, while the informal care network was the main source of tangible support. However, often, participants’ care networks were unable to provide sufficient informational, emotional, esteem, and network support. Peers who also had experienced miscarriage played a crucial role in addressing these gaps in social support. Technology use varied greatly, with smartphone use as the only common denominator. While there was a range of online support sources, participants tended to focus on only a few, and there was no single common preferred source. DISCUSSION: We propose a Miscarriage Circle of Care Model (MCCM), with peer advisors playing a central role in improving communication channels and social support provision. We show how the MCCM can be used to identify gaps in service provision and opportunities where technology can be leveraged to fill those gaps