Taiwanese speech–language therapists’ awareness and experiences of service provision to transgender clients

Background: One of the most influential factors that affect the quality of life of transgender individuals is whether they can be perceived by others to “pass” in their felt gender. Voice and communication style are two important identifying dimensions of gender and many transgender individuals wish to acquire a voice that matches their gender. Evidence shows that few transgender individuals access voice therapy, and that this is caused by their concerns about stigmatization or negative past experiences within healthcare services. In order to address the negative experiences faced by transgender populations we need a better understanding of healthcare services’ current levels of knowledge and LGBT awareness. Some studies of Speech–Language Therapists’ (SLTs’) experience and confidence working with transgender individuals have recently been undertaken in the United States (US). However, little research has been carried out in Asia. Aims: To investigate Taiwanese SLTs’ knowledge, attitudes and experiences of providing transgender individuals with relevant therapy. Method: A cross-sectional self-administered web-based survey hosted on the Qualtrics platform was delivered to 140 Taiwanese SLTs. Results: Taiwanese SLTs were, (i) more familiar with the terminology used to address “lesbian, gay, and bisexual groups” than with “transgender” terminology, (ii) generally positive in their attitudes toward transgender individuals, and (iii) comfortable about providing clinical services to transgender clients. However, the majority of participants did not feel that they were sufficiently skilled in working with transgender individuals, even though most believed that providing them with voice and communication services fell within the SLT scope of practice. Conclusion: It is important for clinicians to both be skilled in transgender voice and communication therapy and to be culturally competent when providing services to transgender individuals. This study recommends that cultural competence relating to gender and sexual minority groups should be addressed in SLTs’ university education as well as in their continuing educational programs

Detection of Lying Electrical Vehicles in Charging Coordination Application Using Deep Learning

The simultaneous charging of many electric vehicles (EVs) stresses the distribution system and may cause grid instability in severe cases. The best way to avoid this problem is by charging coordination. The idea is that the EVs should report data (such as state-of-charge (SoC) of the battery) to run a mechanism to prioritize the charging requests and select the EVs that should charge during this time slot and defer other requests to future time slots. However, EVs may lie and send false data to receive high charging priority illegally. In this paper, we first study this attack to evaluate the gains of the lying EVs and how their behavior impacts the honest EVs and the performance of charging coordination mechanism. Our evaluations indicate that lying EVs have a greater chance to get charged comparing to honest EVs and they degrade the performance of the charging coordination mechanism. Then, an anomaly based detector that is using deep neural networks (DNN) is devised to identify the lying EVs. To do that, we first create an honest dataset for charging coordination application using real driving traces and information revealed by EV manufacturers, and then we also propose a number of attacks to create malicious data. We trained and evaluated two models, which are the multi-layer perceptron (MLP) and the gated recurrent unit (GRU) using this dataset and the GRU detector gives better results. Our evaluations indicate that our detector can detect lying EVs with high accuracy and low false positive rate

Peer crowd-based targeting in E-cigarette advertisements: a qualitative study to inform counter-marketing.

BACKGROUND:Cigarette lifestyle marketing with psychographic targeting has been well documented, but few studies address non-cigarette tobacco products. This study examined how young adults respond to e-cigarette advertisements featuring diverse peer crowds - peer groups with shared identities and lifestyles - to inform tobacco counter-marketing design. METHODS:Fifty-nine young adult tobacco users in California participated in interviews and viewed four to five e-cigarette advertisements that featured characters from various peer crowd groups. For each participant, half of the advertisements they viewed showed characters from the same peer crowd as their own, and the other half of the advertisements featured characters from a different peer crowd. Advertisements were presented in random order. Questions probed what types of cues are noticed in the advertisements, and whether and how much participants liked or disliked the advertisements. RESULTS:Results suggest that participants liked and provided richer descriptions of characters and social situations in the advertisements featuring their own peer crowd more than the advertisements featuring a different peer crowd. Mismatching age or device type was also noted: participants reported advertisements showing older adults were not intended for them. Participants who used larger vaporizers tended to dislike cigalike advertisements even if they featured a matching peer crowd. CONCLUSION:Peer crowd and lifestyle cues, age and device type are all salient features of e-cigarette advertising for young adults. Similarly, educational campaigns about e-cigarettes should employ peer crowd-based targeting to engage young adults, though messages should be carefully tested to ensure authentic and realistic portrayals

Factorization of Discriminatively Trained i-vector Extractor for Speaker Recognition

In this work, we continue in our research on i-vector extractor for speaker verification (SV) and we optimize its architecture for fast and effective discriminative training. We were motivated by computational and memory requirements caused by the large number of parameters of the original generative i-vector model. Our aim is to preserve the power of the original generative model, and at the same time focus the model towards extraction of speaker-related information. We show that it is possible to represent a standard generative i-vector extractor by a model with significantly less parameters and obtain similar performance on SV tasks. We can further refine this compact model by discriminative training and obtain i-vectors that lead to better performance on various SV benchmarks representing different acoustic domains.Comment: Submitted to Interspeech 2019, Graz, Austria. arXiv admin note: substantial text overlap with arXiv:1810.1318

On the Choice of Modeling Unit for Sequence-to-Sequence Speech Recognition

In conventional speech recognition, phoneme-based models outperform grapheme-based models for non-phonetic languages such as English. The performance gap between the two typically reduces as the amount of training data is increased. In this work, we examine the impact of the choice of modeling unit for attention-based encoder-decoder models. We conduct experiments on the LibriSpeech 100hr, 460hr, and 960hr tasks, using various target units (phoneme, grapheme, and word-piece); across all tasks, we find that grapheme or word-piece models consistently outperform phoneme-based models, even though they are evaluated without a lexicon or an external language model. We also investigate model complementarity: we find that we can improve WERs by up to 9% relative by rescoring N-best lists generated from a strong word-piece based baseline with either the phoneme or the grapheme model. Rescoring an N-best list generated by the phonemic system, however, provides limited improvements. Further analysis shows that the word-piece-based models produce more diverse N-best hypotheses, and thus lower oracle WERs, than phonemic models.Comment: To appear in the proceedings of INTERSPEECH 201