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【2020 Application Example】 AI Voice Synthesis Module, Bringing Warmth to Machine Narration

In response to current trends, digital learning and mobile educational materials have attracted widespread attention!

With rapid technological advancements, effectively nurturing professionals who can 'adapt to developmental changes' is a critical concern that many businesses continually consider. Over recent years, various enterprises have progressively integrated 'digital learning' into employee training programs to enhance educational outcomes, thus bringing 'digital learning' and 'mobile educational materials' into the limelight.

Outsourced narration is costly and cannot handle large volumes of demand

Differences in the digital educational material production process before and after the implementation of the AI voice synthesis system

▸ Differences in the digital educational material production process before and after the implementation of the AI voice synthesis system

Strategic Breakthrough Corporation of Taiwan has assisted companies in converting many seminars, physical courses, and training events conducted by public sectors into digital materials in the past years. However, during the conversion process, it required inviting teachers, finding and renting filming locations, and post-production of recordings and videos. During recording, issues such as speakers' nervousness, discomfort in front of cameras, or mispronunciations might lead to poor recording quality or constant retakes.

Though there was an option to provide customer-specific educational material narration, the outsourcing costs were high and could not handle the demand efficiently. Therefore, there was a hope to introduce AI speech synthesis technology and develop an 'Intelligent Voice Synthesis Module' to instantly convert text on slides into natural, human-like voice files, thus saving on narration costs.

Realistic Intelligent Voice Synthesis Module, providing a diversified selection of voices

AI Voice Synthesis Module Illustration

▸ AI Voice Synthesis Module Illustration

Strategic Corporation of Taiwan collaborated with the AI technology team, Magic Cube Digital Ltd., using Tacotron2 combined with WaveNet and Tacotron features. Characters are embedded into Mel-scale spectrogram plots, then a modified WaveNet model acting as the vocoder synthesizes waveform in the time domain from these spectrograms, finally developing an MOS (Mean Opinion Score) for voice quality evaluation that approximates human-like intelligent voice synthesis modules.

This AI Intelligent Voice Synthesis Module, after being tested by testers using the MOS voice quality evaluation standard, received a score of 4.3, meeting the initial project target score of 4.21 and surpassing WaveNet's score of 4.08, thereby demonstrating exceptional effectiveness!

AI Intelligent Voice Synthesis Module, reducing costs and increasing profits, will effectively enhance Taiwan's digital learning industry environment!

Costs have been significantly reduced after the implementation of the AI voice system, and profits have increased relatively

▸ Costs have been significantly reduced after the implementation of the AI voice system, and profits have increased relatively

This AI Intelligent Voice Synthesis Module not only reduces the cost of producing digital educational materials but also solves the difficulties faced by Taiwan's industry, government, and academia in spreading digital educational materials. It can effectively enhance the efficiency of customers in producing digital teaching materials, significantly reduce labor shortages, and cost structural risks, and improve profitability.

Strategic Corporation of Taiwan will also continue to develop the 'Intelligent Transcription Module' and introduce Robotic Process Automation (RPA) to replace the current manual processes, such as captioning, dubbing, and file conversion in the production of digital educational materials, assisting in the transformation and enhancement of the domestic digital learning industry.

「Translated content is generated by ChatGPT and is for reference only. Translation date:2024-05-19」

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[2023 Case Study] AI Steps into Philanthropy: Stylish Tech at Food Banks

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【導入案例】防患於未然 麗臺科技研發心臟衰竭AI辨識技術可及早發現病徵
Preventing Problems Before They Arise: Leadtek Research Develops AI Technology for Early Detection of Heart Failure Symptoms

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people with heart failure worldwide, with 5 million new cases every year In China, nearly 290 million people suffer from cardiovascular diseases, accounting for the second leading cause of death among urban residents around 12 million of these are heart failure patients, accounting for over 59 of cardiac-related deaths The disease course of heart failure is exceptionally long, and both its recurrence and rehospitalization rates are exceedingly high, resulting in medical costs that are twice that of hypertension and five times those of diabetes According to US research statistics, the 30-day mortality rates for patients with myocardial infarction and heart failure are respectively 166 and 111, and the rehospitalization rates within 30 days are 199 and 244 The symptoms of heart failure, because they are similar to those of other diseases such as chronic obstructive pulmonary disease and asthma, have an 185 misdiagnosis rate, which poses a challenging problem for healthcare institutions Leadtek, a major graphics card manufacturer, has been investing in the medical and healthcare sector since 2000 Following two heart attacks in 2011 and 2015 experienced by Chairman Lu Kunshan, Leadtek has focused on health big data, independently developing AI technology for heart failure recognition This AI application reads patients' electrocardiograms and phonocardiograms to perform anomaly detection and model prediction of heart failure risk, enabling early detection of disease symptoms Leadtek independently developed heart failure AI recognition technology to predict medical history and risk Leadtek's independently developed heart failure AI recognition technology has the following three judgment functions 1 Prediction of heart failure history Classifies electrocardiogram and phonocardiogram data into 'with hospitalization history of heart failure' and 'no history of heart failure' 2 Risk prediction of heart failure Provides a predictive risk value of heart failure occurrence based on the electrocardiogram and phonocardiogram data 3 Prediction of heart failure recurrence risk For patients with heart failure, it reads their phonocardiogram and electrocardiogram data, assessing the risk prediction of heart failure recurrence Leadtek states that the application of heart failure AI recognition technology can assist doctors in making more efficient and accurate diagnoses, facilitating subsequent medical treatment or referrals for patients As an instance, in studies of heart failure patients discharged from Taipei Veterans General Hospital, using the EMAT Electromechanical Activation Time index and SDI Systolic Dysfunction Index calculated by the synchronized electrocardiography-acoustic device as treatment guidelines resulted in a higher survival rate compared to those treated based on traditional symptoms This research has also been published in the authoritative international cardiology journal JACC, receiving recognition in the international market System manufacturers can apply heart failure AI recognition technology for other value-added applications Leadtek states that cooperating system manufacturers can choose to build their own heart failure AI risk prediction engine, uploading their system's electrocardiogram and phonocardiogram data to Leadtek's heart failure AI risk prediction engine, which then returns risk prediction values for integration by system manufacturers cooperating manufacturers as a value-added application input Not just for clinical use, the heart failure AI risk prediction engine can also be extended for use at home or in the workplace Additionally, this system can be extended to other applications, including One, hospital outpatient screening Doctors can use the electrocardiogram and phonocardiogram recorder along with the heart failure AI risk prediction model to conduct a 10-second rapid test in outpatient and emergency departments to assess a patient's cardiac history and heart failure risk Two, discharge risk assessment Doctors can use the electrocardiogram and phonocardiogram recorder along with the heart failure AI risk prediction model to assess the heart failure risk during a patient's hospital stay The test data can serve as a pre-discharge risk assessment and prognostic indicator Three, continuous home care Patients can use the electrocardiogram and phonocardiogram recorder, wearable electrocardiogram recorder, and transmit through a home transmission box gateway to measure electrocardiogram and phonocardiogram signals at home and upload them to the amor health cloud platform for heart failure AI risk prediction analysis Patients can manage their health autonomously via an APP, reviewing historical physiological trends disease management nurses can manage member health through the health management backend Web Four, home rehabilitation training Patients can wear a health bracelet to monitor activity, fatigue, circulation, and sleep, autonomously managing their health through the mobile APP and observing the risk of heart failure, engaging in exercise and rehabilitation training to aid in swift recovery The heart failure AI recognition technology system can also be extended to employee home care applications Additionally, in factories or offices, this system can also achieve employee health management goals, with applications including One, workplace safety units Provide employees with wearable electrocardiogram recorders before they start work duties Two, physiological monitoring for business executors While executing business duties or training, employees wear wearable electrocardiogram recorders for fatigue warnings, signaling whether physiological conditions allow continued execution of tasks Task segments can use data transmission boxes or apps to upload physiological monitoring information to the health management platform, assessing the heart failure risk for operations staff, with test data serving as an indicator for enterprise resource human units and public safety Three, workplace physiological monitoring center care The workplace physiological monitoring center can inspect and record employees' historicalphysiological trends through the health cloud platform Four, workplace nursing units Nursing units receiving instructions from the physiological monitoring center can provide health management advice based on employees' physiological trends nursing centers can manage employee health through the health management backend Web Five, employees can wear health bracelets to monitor activity, fatigue, circulation, and sleep, autonomously managing their health and observing the risk of heart failure through the mobile APP, engaging in exercise and rehabilitation training to aid in rapid recovery Workplace application of heart failure cloud care and big data center diagram「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」