150 research outputs found
Chinese literary works translated into Baba Malay: a bibliographical study
Get PDFAnalyses 68 unique titles of Baba translated works published between 1889 and 1950. The titles are held in the libraries of the University of Malaya (UM), Science University Malaysia (USM), National University of Malaysia (UKM), the Dewan Bahasa dan Pustaka (DBP), National University of Singapore (NUS), National Library of Singapore (NLS) and the British Library (BL). The results reveal three periods of active publication of Baba translated works. A total of 18 works were translated before World War I, followed by 10 just after the war, 39 titles were published before the break of the World War II and 1 was identified in 1950. There were 103 persons involved in the 68 translated works, some of whom are responsible for more than one title. The most prominent translators were Chan Kim Boon, Wan Boon Seng, Seow Chin San and Lee Seng Poh. Some of the translators were also be editors, illustrators or editors. There were 31 publishers and 21 printing presses involved, all were located in Singapore. The most active publishers were Wan Boon Seng, Kim Seck Chy Press and Nanyang Romanised Malay Book Co. The translated works mainly cover historical classical Chinese stories, chivalrous stories, romances, folklore and legends. The titles were priced between 10 cents to 2 dollars in Straits currency. The University of Malaya Library held the largest number of unique title (62) out of which 15 were unique titles
State-of-the-Art Imaging Techniques in Metastatic Spinal Cord Compression
No full textSIMPLE SUMMARY: Metastatic Spinal Cord Compression (MSCC) is a feared complication in oncology patients due to its potential for severe pain, permanent neurological disability and mechanical instability of the spine. This narrative review, conducted by keyword searches in PubMed and Google Scholar databases, aims to describe the important role of imaging in MSCC diagnosis and treatment. Diagnosis is typically achieved via Magnetic Resonance Imaging (MRI), although Computed Tomography (CT) Myelogram and conventional CT imaging can be performed in certain clinical situations. Metal artifact reduction techniques for MRI and CT are continually being researched to facilitate imaging in MSCC patients with spinal implants. Imaging also has an important role in pre-treatment planning, in-room image-guidance, and post-treatment follow-up for MSCC patients treated with stereotactic body radiotherapy. Recent advances in deep learning tools for image analysis can reduce the time to MSCC diagnosis, enabling earlier treatment for superior functional outcomes. ABSTRACT: Metastatic Spinal Cord Compression (MSCC) is a debilitating complication in oncology patients. This narrative review discusses the strengths and limitations of various imaging modalities in diagnosing MSCC, the role of imaging in stereotactic body radiotherapy (SBRT) for MSCC treatment, and recent advances in deep learning (DL) tools for MSCC diagnosis. PubMed and Google Scholar databases were searched using targeted keywords. Studies were reviewed in consensus among the co-authors for their suitability before inclusion. MRI is the gold standard of imaging to diagnose MSCC with reported sensitivity and specificity of 93% and 97% respectively. CT Myelogram appears to have comparable sensitivity and specificity to contrast-enhanced MRI. Conventional CT has a lower diagnostic accuracy than MRI in MSCC diagnosis, but is helpful in emergent situations with limited access to MRI. Metal artifact reduction techniques for MRI and CT are continually being researched for patients with spinal implants. Imaging is crucial for SBRT treatment planning and three-dimensional positional verification of the treatment isocentre prior to SBRT delivery. Structural and functional MRI may be helpful in post-treatment surveillance. DL tools may improve detection of vertebral metastasis and reduce time to MSCC diagnosis. This enables earlier institution of definitive therapy for better outcomes
Biochemical “decoding” of breast ultrasound images with optoacoustic tomography fusion: First-in-human display of lipid and collagen signals on breast ultrasound
No full textTo date, studies which utilized ultrasound (US) and optoacoustic tomography (OT) fusion (US-OT) in biochemical differentiation of malignant and benign breast conditions have relied on limited biochemical data such as oxyhaemoglobin (OH) and deoxyhaemoglobin (DH) only. There has been no data of the largest biochemical components of breast fibroglandular tissue: lipid and collagen. Here, the authors believe the ability to image collagen and lipids within the breast tissue could serve as an important milestone in breast US-OT imaging with many potential downstream clinical applications. Hence, we would like to present the first-in-human US-OT demonstration of lipid and collagen differentiation in an excised breast tissue from a 38-year-old female
The design and verification of malay text to speech synthesis system
Get PDFSynthetic or artificial speech has been developed steadily during the last decades. The intelligibility of synthetic speech has reached an adequate level for most applications, especially for communication impaired people. The first objective of this work is to design and develop a Malay Text to Speech (Malay TTS) system. This will include the design of Malay TTS diphone database, tokenization rules, letter-to-sound rules, Malay lexicon and prosody rules. Other focus of this work is to design a set of test methods specifically for verifying Malay TTS performance. This work has produced a diphone database with 1629 diphone file in residual-exited LPC (RELP) format and its total size is around 3.4 Mega bytes. Besides that, this work also has identify the possible tokenization area in Malay TTS and develop a digit tokenization for Malay TTS as the basic for further development of more complete tokenization rules. This work also has produced complete letter-to-sound (LTS) rules for Malay primary word that has high accuracy and almost 100 percent accuracy. A set of lexicon containing 1000 most common use Malay words also being setup as complement to the LTS coverage. A set of a prosody rules using a CART tree has been setup as the preliminary study in prosody design for Malay TTS. Finally, the very first try in designing the testing methods and procedures for Malay TTS has been completed. It will provide a more complete technique in verifying the performance of Malay TTS that will become the benchmark for Malay TTS evaluation and improvement in future
Corpus design for Malay corpus-based speech synthesis system
No full textProblem statement: Speech corpus is one of the major components in corpus-based synthesis. The quality and coverage in speech corpus will affect the quality of synthesis speech sound. Approach: This study proposes a corpus design for Malay corpus-based speech synthesis system. This includes the study of design criteria in corpus-based speech synthesis, Malay corpus based database design and the concatenation engine in Malay corpus-based synthesis system. A set of 10 millions digital text corpuses for Malay language has been collected from Malay internet news. This text corpus had been analyzed using word frequency count to find out all high frequency words to be used for designing the sentences for speech corpus. Results: Altogether 381 sentences for speech corpus had been designed using 70% of high frequency words from 10 million text corpus. It consists of 16826 phoneme units and the total storage size is 37.6Mb. All the phone units are phonetically transcribed to preserve the phonetic context of its origin that will be used for phonetic context unit. This speech corpus had been labeled at phoneme level and used for variable length continuous phoneme based concatenation. Speech corpus is one of the major components in corpus-based synthesis. The quality and coverage in speech corpus will affect the quality of synthesized speech sound. Conclusion/Recommendation: This study has proposed a platform for designing speech corpus especially for Malay Text to Speech which can be further enhanced to support more coverage and higher naturalness of synthetic speech
Auto segmentation for Malay speech corpus
No full textAbstract-This paper deals with the automatic segmentation
of Malay continuous speech database. Auto segmentation
is a process of producing a sequence of discrete utterance with particular characteristics remaining constant within each one. In terms of quality, hand crafted segmentation would be the best method. However, due to the large database size, manual speech segmentation and labeling become tremendous. It is time consuming and error prone. Besides, even if the
database is segmented by an expert, the segmentation rule may become subjective and not reproducible. Inconsistency result may occur from different linguistic experts. Thus, an automated segmentation rule was drawn to consistently segment the large scale database with satisfactory level of quality. Automated segmentation of Malay Language syllable is not a tough task because all syllables in Malay Language are pronounced almost equally and moreover it is not a tonal language like English. The manipulation and identification of the segment boundaries of Malay Language is straight forward and easy to understand.
For the segmentation, the HMM based approach with adapted
Viterbi force alignment technique is used. Composite HMM with Baum Welch reestimation was utilized to ease the process of phonetic segmentation. All the data from the database was fed into the segmentation tool directly without prior trained sample for pre-training purpose. For the design of the sentence coverage of the database, the scripts are consisting of 1000 sentences. 620 sentences are selected from primary school Malay Language
text book and 380 sentences were computed using the 70%
highest frequency words that appear in the 10 million words online digital text. This configuration of Malay Language script already promises a phonetically balanced database which covers all the vowels and consonants. The objective evaluation method is used to identify the performance. The result from the autosegmentation
was verified to obtain the accuracy degree and
overall quality. The result was tested perceptually and it is proven to have satisfactory high quality
Statistical parametric speech synthesis of Malay language using found training data
No full textThe preparation of training data for statistical parametric speech synthesis can be sophisticated. To ensure the good quality of synthetic speech, high quality low noise recording must be prepared. The preparation of recording script can be also tremendous from words collection, words selection and sentences design. It requires tremendous human effort and takes a lot of time. In this study, we used alternative free source of recording and text such as audio-book, clean speech and so on as the training data. Some of the free source can provide high quality recording with low noise which is suitable to become training data. Statistical parametric speech synthesis method applying Hidden Markov Model (HMM) has been used. To test the reliability of synthetic speech, perceptual test has been conducted. The result of naturalness test is fairly reasonable. The intelligibility test showed encouraging result. The Word Error Rate (WER) for normal synthetic sentences is below 15% while for Semantically Unpredictable Sentences (SUS) is averagely in 30%. In short, using free and ready source as training data can leverage the process of preparing training data while obtaining motivating synthetic result
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