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Sexual violence is a severe and chronic occurrence around the world that has not been resolved. The stigmatized nature of sexual violence has forced victims and survivors to accept prejudiced accusations cultivated from discriminatory norms when they are never at fault nor responsible for such violations against their sexuality. LAW-U is an Artificial Intelligence (AI) chatbot that gives legal guidance to survivors of sexual violence by recommending the most relevant Supreme Court decisions to the survivors' situations. In Thai, "LAW-U" − pronounced similarly to "รออยู ่ " − means "I will wait for you", which signifies the chatbot's unconditional support to the user. 182 Thai Supreme Court cases of sexual violence, relating to Sections 276, 277, 278, and 279 of the Criminal Code, were used to develop Natural Language Processing (NLP) pipelines for LAW-U. Legal experts then generated mock-up dialogs from Supreme Court decisions which became the conversations used to train LAW-U. The computation of the similarity scores and the calculation of combined percentages of common keywords and keywords' synonyms were completed to increase the model's accuracy. When applying the model to the hold-out testing dataset, the accuracy was 88.89% for an exact match between the user's input and the Supreme Court case − this confirmed that LAW-U was ready for real-life application. LAW-U's unique design hopes to act as a precedent for other works at home and abroad to perpetuate awareness of sexual violence and eliminate any tolerance against these crimes by empowering sexual violence victims and survivors to reaffirm their inherent rights.

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Word recognition score (WRS) is one of the measuring techniques used in speech audiometry, a part of a routine audiological examination. The test’s accuracy is crucial and largely depends on the test materials. With emphasis on phonetic balance, test-retest reliability, inter-list equivalency, and symmetrical phoneme occurrence, Thammasat University Phonetically Balanced Word Lists 2014 (TU PB’14) were created with five different lists, each with 25 Thai monosyllabic words. TU PB’14 reflects Thai phoneme distribution based on large-scale written Thai corpora, InterBEST [1]. To evaluate its validity and test-retest reliability, the lists were given at five intensity levels (15–55 dB HL) in test and retest sessions to 30 normal-hearing subjects. The differences in performance between the two sessions are not significantly large and correlation coefficients at the linear regions are all positive. Analysis of listeners’ errors, including sequence recurrences, was carried out. Errors occurred predominantly in the case of initials, followed by finals and lexical tones. Confusion patterns of initials, finals, and tones are in line with those found for Thai speech sounds in noise condition. Interestingly, vowels are found to be most resistant to confusion. Finally, possible effect of lexical frequency is examined and discussed.

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