Rhythm-Flexible Voice Conversion without Parallel Data Using Cycle-GAN over Phoneme Posteriorgram Sequences

Speaking rate refers to the average number of phonemes within some unit time, while the rhythmic patterns refer to duration distributions for realizations of different phonemes within different phonetic structures. Both are key components of prosody in speech, which is different for different speakers. Models like cycle-consistent adversarial network (Cycle-GAN) and variational auto-encoder (VAE) have been successfully applied to voice conversion tasks without parallel data. However, due to the neural network architectures and feature vectors chosen for these approaches, the length of the predicted utterance has to be fixed to that of the input utterance, which limits the flexibility in mimicking the speaking rates and rhythmic patterns for the target speaker. On the other hand, sequence-to-sequence learning model was used to remove the above length constraint, but parallel training data are needed. In this paper, we propose an approach utilizing sequence-to-sequence model trained with unsupervised Cycle-GAN to perform the transformation between the phoneme posteriorgram sequences for different speakers. In this way, the length constraint mentioned above is removed to offer rhythm-flexible voice conversion without requiring parallel data. Preliminary evaluation on two datasets showed very encouraging results.Comment: 8 pages, 6 figures, Submitted to SLT 201

User Acceptance of E-Government Services

In order to provide more accessible, accurate, real-time information for citizens, government E-services, such as information kiosks, have been set up in many public places. Although the public sector has promoted this E-Government service for many years, its uses and achievements are few. Therefore, this paper explores the key factors of user acceptance through a research survey and by gathering empirical evidence based on the Unified Theory of Acceptance and the Use of Technology (UTAUT). Data collected from 244 respondents was tested against the research model. The results lead us to make several recommendations for the public sector and policy-makers to use as guidelines for the future development of this service

Adapting Software Teams to the New Normal: An Early Case Study of Transitioning to Hybrid Work Under COVID-19

In the wake of the COVID-19 pandemic, many studies have begun to address what some refer to as the "new normal," comprising hybrid arrangements of employees working from home and working at the office with varying schedule arrangements. While many of the studies to date addressed how employees coped with work-from-home, we sought to investigate how employees dealt with a transition to the new normal of hybrid arrangements. To shed light on this topic, we conducted a survey-based case study at one office location of a large, multinational software corporation. The site sought to transition employees fully working from home to working two days remotely and three predefined days in their shared workspace. Our survey results indicated a substantial decline in work satisfaction since the beginning of this transition, which can be explained by diverse work preferences. Furthermore, some software developers felt frustrated during this transition time; they described challenges they underwent and proposed potential solutions. In this paper, we present our lessons learned in this case study and describe some actionable recommendations for practitioners facing such transitions