44 research outputs found

    Into-TTS : Intonation Template based Prosody Control System

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    Intonations take an important role in delivering the intention of the speaker. However, current end-to-end TTS systems often fail to model proper intonations. To alleviate this problem, we propose a novel, intuitive method to synthesize speech in different intonations using predefined intonation templates. Prior to the acoustic model training, speech data are automatically grouped into intonation templates by k-means clustering, according to their sentence-final F0 contour. Two proposed modules are added to the end-to-end TTS framework: intonation classifier and intonation encoder. The intonation classifier recommends a suitable intonation template to the given text. The intonation encoder, attached to the text encoder output, synthesizes speech abiding the requested intonation template. Main contributions of our paper are: (a) an easy-to-use intonation control system covering a wide range of users; (b) better performance in wrapping speech in a requested intonation with improved pitch distance and MOS; and (c) feasibility to future integration between TTS and NLP, TTS being able to utilize contextual information. Audio samples are available at https://srtts.github.io/IntoTTS.Comment: Submitted to INTERSPEECH 202

    spatio temporal contextualization of queries for microtexts in social media mathematical modeling

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    Abstract In this paper, we present our ongoing project on query contextualization by integrating all possible IoT-based data sources. Most importantly, mobile users are regarded as the IoT sensors which can be the textual data sources with spatio-temporal contexts. Given a large amount of text streams, it has been difficult for the traditional information retrieval systems to conduct the searching tasks. The goal of this work is i ) to understand and process microtexts in social media (e.g., Twitter and Facebook), and ii ) to reformulate the queries for searching for relevant microtexts in these social media

    Wireless Kitchen Fire Prevention System Using Electrochemical Carbon Dioxide Gas Sensor for Smart Home

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    This paper presents a wireless kitchen fire prevention system that can detect and notify the fire risk caused by gas stoves. The proposed system consists of two modules. The sensor module detects the concentration of carbon dioxide (CO2) near the gas stove and transmits the monitoring results wirelessly. The alarm module, which is placed in other places, receives the data and reminds the user of the stove status. The sensor module uses a cost-efficient electrochemical CO2 sensor and embeds an in situ algorithm that determines the status of the gas stove based on the measured CO2 concentration. For the wireless communication between the modules, on-off keying (OOK) is employed, thereby achieving a longer battery lifetime of the alarm module, low cost, and simple implementation. To increase the lifetime further, a wake-up function based on passive infrared (PIR) sensing is employed in the alarm module. Our system can successfully detect the on state of the stove within 40 s and the off state within 200 s. Thanks to the low-power implementation, in situ algorithm, and wake-up function, the alarm module’s expected battery lifetime is extended to about two months

    Finger motion detection glove toward human-machine interface

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    Finger motion capturing systems have a wide variety of applications such as telerobotics, rehabilitation, and avatar control. While commercial devices are too costly, studies on such systems are either impractical to use or have speed limitations. This paper proposes a practical version of the glove-based finger motion capturing system. This system can achieve a capture speed high enough to represent smooth and swift finger motions with considerably low cost. The system provides a speed of 54 Hz for 14 channels with their signal conditioning circuits and flexible strain sensors. In addition, the system has one-touch calibration mode for baseline cancellation, which makes it user-friendly more
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