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Although pre-low raising (PLR) has been extensively studied as a type of contextual tonal variation, its underlying mechanism is barely understood. This paper explored the effects of phonetic vs phonological duration on PLR in Cantonese and Thai and examined how speech rate and vowel quantity interact with its realization in these languages, respectively. The results for Cantonese revealed that PLR always occurred before a large falling excursion (i.e., high-low); in other tonal contexts, it was observed more often in faster speech. In the Thai corpus, PLR also occurred before large falling excursions, and there was more PLR in short vowels. These results are discussed in terms of possible accounts of the underlying mechanism of PLR.

The nature of English diphthongs has been much disputed. Bynow, the most influential account argues that diphthongs arephoneme entities rather than vowel combinations. However,mixed results have been reported regarding whether the rate offormant transition is the most reliable attribute in the perceptionand production of diphthongs. Here, we used computationalmodelling to explore the underlying forms of diphthongs. Wetested the assumption that diphthongs have dynamicarticulatory targets by training an articulatory synthesiser witha three-dimensional (3D) vocal tract model to learn Englishwords. An automatic phoneme recogniser was constructed toguide the learning of the diphthongs. Listening experiments bynative listeners indicated that the model succeeded in learninghighly intelligible diphthongs, providing support for thedynamic target assumption. The modelling approach paves anew way for validating hypotheses of speech perception andproduction.

In paper production, a jumbo reel is cut into multiple intermediate rolls, and each intermediate roll is then sheeted as finished goods. This problem is called a cutting stock problem and is proven to be NP-hard. The objective is to minimize material waste or trim loss from all the cuttings. In the case that any intermediate roll is not entirely used for its associated order, the intermediate roll itself could turn to be a dead stock. We use the concept of universal sizes of intermediate rolls to eliminate the dead stock. A pre-defined number of universal sizes of intermediate rolls is to be used to serve all the orders. The problem is solved using Reinforcement Artificial Bee Colony algorithm with Integer Linear Programming subroutine. This proposed approach is then tested with a set of 1,055 orders and 127 different sizes of sheet papers from a paper manufacturer. The results reveal that our method outperforms other algorithms. Our method offers the total trim loss of 3.51%, compared to the trim loss reported by the industry of at least 5%. This approach not only reduces the number of partially cut rolls, but also decreases the number of the jumbo reels needed to serve all the orders. Therefore, both the inventory cost and material cost can be saved.

While the acoustic vowel space has been extensively studied in previous research, little is known about the high-dimensional articulatory space of vowels. The articulatory imaging techniques are limited to tracking only a few key articulators, leaving the rest of the articulators unmonitored. In the present study, we attempted to develop a detailed articulatory space obtained by training a 3D articulatory synthesizer to learn eleven British English vowels. An analysis-by-synthesis strategy was used to acoustically optimize vocal tract parameters that represent twenty articulatory dimensions. The results show that tongue height and retraction, larynx location and lip roundness are the most perceptually distinctive articulatory dimensions. Yet, even for these dimensions, there is a fair amount of articulatory overlap between vowels, unlike the fine-grained acoustic space. This method opens up the possibility of using modelling to investigate the link between speech production and perception.

This paper proposes to develop a model to detect the text semantic similarity in Thai by using a Siamese neural network with MaLSTM. As the text's intent is varied and hard to analyze, thus making the comparison between two sentences for the semantics similarity challenging. Our project wants to find similar intent of two questions by comparing the question between frequently asked questions (FAQs) and input questions from customers via Facebook Messenger of computer engineering at the King Mongkut's University of Technology Thonburi (CPE KMUTT). The data gather manually from the Pantip, CPE KMUTT FAQ, and Wikipedia, which create a dataset and data corpus. Although, the overall score performance of the model is not as we expected. Our model shows promising results, as it can detect between two questions' intent similarity. Results suggest that with the increase of the data, the model should have great potential for finding semantic similarity.

Word segmentation is an important preprocessing step in natural language processing applications, particularly in languages with no demarcation indicators including Thai. A simple method like dictionary-based segmentation does not consider the context of the sentence. This paper proposes an attention-based deep learning approach for Thai word segmentation. With the help of attention, the model can learn character correlations across the entire sentence without gradient vanishing or gradient explode problems and tokenize them into word vectors. The goal of this research is to test three different types of attention mechanisms to determine the effectiveness of word tokenization. The visualization of attention for each attention mechanism is also included as an outcome.

COVID-19 is undeniably one of the worst incidents in the 21st century. There are a wide variety of factors that impact the spreading of COVID-19. This paper presents the study of relations of how public policy implementation might affect the onset and the spread of COVID-19 cases. Cluster analysis was employed to identify data patterns associating with the policy implementation profiles. The results suggest that the effectiveness of policy adoption relates to the onset spreading of COVID-19. This also indicates that the decision of public administrators was critical in the latter stage of the pandemic situation management.

This paper presents a computational study of Thai legal documents using text mining and network analytic approach. Thai legal systems rely much on the existing judicial rulings. Thus, legal documents contain complex relationships and require careful examination. The objective of this study is to use text mining to model relationships between these legal documents and draw useful insights. A structure of document relationship was found as a result of the study in forms of a network that is related to the meaningful relations of legal documents. This can potentially be developed further into a document retrieval system based on how documents are related in the network.

Prosody is an essential aspect of speech, as it carries both lexical and non-lexical information. A conventional approach for studying speech prosody is to collect and analyze F0 data based on certain hypotheses and then develop a theory based on the observation as the final conclusion of the study. This process is however far from complete, as the developed theory has not been actually tested for its ability to predict actual acoustic data. This paper presents PENTATrainer2, a prosody modeling tool based on the parallel encoding and target approximation framework. PENTATrainer2 can facilitate prosody studies in testing hypotheses and theories against speech data by using an automatic analysis-by-synthesis and stochastic learning algorithm. Users can flexibly design the annotation scheme based on their own hypotheses and determine whether the hypothesized categories can lead to accurate synthetic F0 contours. PENTATrainer2 consists of three main components: multi-layer annotation, target approximation, and stochastic optimization. First, acoustic data are annotated in parallel layers, each of which corresponds to a functional category that may affect F0 contours. These layers are then compiled into unique functional combinations. The combinations represent underlying invariant representations of communicative functions and their interaction with each other. Target approximation parameters of each combination are then learned through analysis-by-synthesis and stochastic optimization. Pilot tests of PENTATrainer 2 have been conducted on Thai, Mandarin and English. The results demonstrate not only high accuracy of the synthesized F0 contours but also distinctive contrasts in the distribution of pitch target parameters. This indicates the effectiveness of PENTATrainer2 in modeling speech prosody.

This paper presents an acoustic-to-articulatory mapping of a three-dimensional theoretical vocal tract model using deep learning methods. Prominent deep learning-based network structures are explored and evaluated for their suitability in capturing the relationship between acoustic and articulatory-oriented vocal tract parameters. The dataset was synthesized from VocalTractLab, a three-dimensional theoretical articulatory synthesizer, in forms of the pairs of acoustic, represented by Mel-frequency cepstral coefficients (MFCCs), and articulatory signals, represented by 23 vocal tract parameters. The sentence structure used in the dataset generation were both monosyllabic and disyllabic vowel articulations. Models were evaluated using the root-mean-square error (RMSE) and R-squared (R2). The deep artificial neural network architecture (DNN), regulating using batch normalization, achieves the best performance for both inversion tasks, RMSE of 0.015 and R2 of 0.970 for monosyllabic vowels and RMSE of 0.015and R2 of 0.975 for disyllabic vowels. The comparison, between a formant of a sound from inverted articulatory parameters and the original synthesized sound, demonstrates that there is no statistically different between original and estimated parameters. The results indicate that the deep learning-based model is effectively estimated articulatory parameters in a three-dimensional space of a vocal tract model.

Named Entity Recognition (NER) is a handy tool for many natural language processing tasks to identify and extract a unique entity such as person, location, organization and time. In English and Chinese, NER has been thoroughly researched and is able to be applied in more practical settings. Its development in Thai is still limited because of rare resources and language difficulties such as the lack of boundary indicator for words, phrases and sentences. In this paper, we present an application of Bi-LSTM-CRF with word/character level representation, to solve this problem. Firstly, we prepared texts by tokenizing a sentence to a bunch of words. We then prepared word representation and Bi-LSTM character representation. In the end, we built a recurrent neural network combined with CRF to learn the sequence of text and extract the knowledge to build NER recognition to overcome this problem. Our model was evaluated by the NER opensource corpus from a Facebook group ThaiNLP. The results of our model yielded precision, recall, and F1 at 91.79%, 91.51% and 91.65% respectively.

Image processing and computer vision algorithms are very computationally intensive and could not be implemented on low power microcontroller used in embedded and small IoT devices. Performing computation on the cloud is not practical for always-on real-time operations and deploying a high-end microcontroller or a microprocessor consume too much power and are generally too expensive to fit into these small systems. In this paper, we propose a new programming workflow using reusable hardware modules and a graphical programming interface for implementing a complete image processing system on an FPGAs which overcomes the steep learning curve of tradition FPGAs design tool. The design can be deployed onto the low-ended FPGAs in a similar price point as a mid-range microcontroller. To demonstrate our proposed framework, we have implemented a number of image transformation operations on an FPGAs development board with Lattice iCE40 Ultra Plus FPGA and a tiny camera module. Results have shown that our designs can fit in a low-ended FPGAs while performing 5.5-345x faster and consuming 2.4-4x less power compared to current state-of-the-art microcontroller used in small embedded and IoT devices.