Google Speech Recognition Research Articles

Usability evaluation of navigation tasks by people with intellectual disabilities: a Google and SAPO comparative study regarding different interaction modalities

This paper presents a case study regarding the usability evaluation of navigation tasks by people with intellectual disabilities. The aim was to investigate the factors affecting usability, by comparing their user-Web interactions and underline the difficulties observed. For that purpose, two distinct study phases were performed: the first consisted in comparing interaction using two different search engines' layouts (Google and SAPO) and the second phase consisted in a preliminary evaluation to analyze how users performed the tasks with the usual input devices (keyboard and mouse) and provide an alternative interface to help overcome possible interaction problems and enhance autonomy. For the latter, we compared two different interfaces: a WIMP-based one and speech-based one. The main results obtained showed that users had a better performance with Google (with a simpler layout) than with SAPO (with a complex layout), and despite displaying a good keyboard handling ability, they did not show autonomy using this input device (due to the need for reading/writing when handling this device). In this perspective, Google's speech recognition application could indeed be considered an alternative for interaction. However, we found that the speech recognition interface is not as robust as it should be: it could be more precise and less prone to errors due to poor word pronunciation. After this two-phased study, we think we may be able to infer some recommendations to be used by developers in order to create more intuitive layouts for easy navigation regarding this group of people, and thereby facilitate digital inclusion.

Autonomous Tour Guide Robot using Embedded System Control

This paper describes an interactive autonomous tour guide robot designed to guide visitors through Asia Pacific University Engineering Labs. Although tour guide robots with various self-localization abilities such as mapping has been introduced in the past, these technologies performance still remain challenged by indoor navigation obstacles. The current approach consists of implementing a low cost autonomous indoor tour guide robot running on an embedded system which is the Raspberry pi 2. The autonomous navigation is achieved through wall following using ultrasonic sensors and image processing using a simple webcam. The bitwise image processing comparison method introduced is writing in OpenCV and runs on the Raspberry pi. It grabs images and look for the tags to identify each lab. A recognition accuracy of 98% was attained during the navigation testing in the labs. The user interaction was achieved through voice recognition on an android tablet placed on top of the robot. Google speech recognition API's was used for the communication between the robot and the visitors.

히어 캠 임베디드 플랫폼 설계

In this paper, we implemented the HearCAM platform with Raspberry PI B+ model which is an open source platform. Raspberry PI B+ model consists of dual step-down (buck) power supply with polarity protection circuit and hot-swap protection, Broadcom SoC BCM2835 running at 700MHz, 512MB RAM solered on top of the Broadcom chip, and PI camera serial connector. In this paper, we used the Google speech recognition engine for recognizing the voice characteristics, and implemented the pattern matching with OpenCV software, and extended the functionality of speech ability with SVOX TTS(Text-to-speech) as the matching result talking to the microphone of users. And therefore we implemented the functions of the HearCAM for identifying the voice and pattern characteristics of target image scanning with PI camera with gathering the temperature sensor data under IoT environment. we implemented the speech recognition, pattern matching, and temperature sensor data logging with Wi-Fi wireless communication. And then we directly designed and made the shape of HearCAM with 3D printing technology.

The simulation of an emotional robot implemented with fuzzy logic

Recently, researchers have tried to better understand human behaviors so as to let robots act in more human ways, which means a robot may have its own emotions defined by its designers. To achieve this goal, in this study, we designed and simulated a robot, named Shiau_Lu, which is empowered with six universal human emotions, including happiness, anger, fear, sadness, disgust and surprise. When we input a sentence to Shiau_Lu through voice, it recognizes the sentence by invoking the Google speech recognition method running on an Android system, and outputs a sentence to reveal its current emotional states. Each input sentence affects the strength of the six emotional variables used to represent the six emotions, one corresponding to one. After that, the emotional variables will change into new states. The consequent fuzzy inference process infers and determines the most significant emotion as the primary emotion, with which an appropriate output sentence as a response of the input is chosen from its Output-sentence database. With the new states of the six emotional variables, when the robot encounters another sentence, the above process repeats and another output sentence is then selected and replied. Artificial intelligence and psychological theories of human behaviors have been applied to the robot to simulate how emotions are influenced by the outside world through languages. In fact, the robot may help autistic children to interact more with the world around them and relate themselves well to the outside world.