The Implementation of a Vocabulary and Grammar for an Open-Source Speech-Recognition Programming Platform

2015 ◽  
Author(s):  
Jean K. Rodriguez-Cartagena ◽  
Andrea C. Claudio-Palacios ◽  
Natalia Pacheco-Tallaj ◽  
Valerie Santiago González ◽  
Patricia Ordonez-Franco
Keyword(s):  
Speech Recognition ◽  
Open Source

scholarly journals Open Source German Distant Speech Recognition: Corpus and Acoustic Model

2015 ◽  
pp. 480-488 ◽  
Author(s):  
Stephan Radeck-Arneth ◽  
Benjamin Milde ◽  
Arvid Lange ◽  
Evandro Gouvêa ◽  
Stefan Radomski ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Open Source ◽  
Acoustic Model

scholarly journals A survey of open-source speech recognition software for voice actuated control

2019 ◽  
Vol 17 ◽  
Author(s):  
V.S. Derevianchenko ◽  
◽  
T.O. Biloborodova ◽  
I.S. Skarga-Bandurova ◽  
P.S. Fursa ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Open Source ◽  
Speech Recognition Software ◽  
Recognition Software

scholarly journals Ferramentas e recursos disponíveis para reconhecimento de fala em Português Brasileiro

2021 ◽  
Author(s):  
Matheus De Lima ◽  
Bruna Coelho ◽  
Fabrício Takigawa
Keyword(s):  
Speech Recognition ◽  
Open Source ◽  
Industry 4.0 ◽  
Recognition Performance ◽  
Business Rule ◽  
Great Demand

Speech recognition allows natural communication between the humansand machines. With Industry 4.0 there is a great demand forsystems that perform this task, since human-machine integrationsare increasingly attractive. Currently, there are several tools and resourcesthat perform this activity, with some companies providingtheir audio recognition services through the Application ProgrammingInterface, such as Microsoft, Google, IBM and Wit. On theother hand, there are offline libraries and open source that can alsobe explored like Vosk. Each company has its business rule and itsspecificity, in this sense it is difficult to know which is the most interestingfor each situation. Thus, a comparison was made betweenspeech recognition services in terms of usability, limitation andprecision. In the comparison, speech recognition performance metricswere used in a set of audios, using the programming languagePython.

scholarly journals Implementasi Speech Recognition Pada Aplikasi E-Prescribing Menggunakan Algoritme Convolutional Neural Network

2021 ◽  
Vol 5 (2) ◽  
pp. 460
Author(s):  
Nur Azis ◽  
Herwanto Herwanto ◽  
Fathurrahman Ramadhani
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Speech Recognition ◽  
Convolutional Neural Network ◽  
Open Source ◽  
Basic Method ◽  
Neural Network Method ◽  
The Neural Network ◽  
Network Method

The process of manually prescribing drugs by doctors can cause several problems, including doctors not knowing what drugs are available and it takes time to find out what drugs are available in the pharmacy. Speech recognition is now widely used in various ways, which can help facilitate work. The application of speech recognition can be done in the e-prescribing application with the neural network method using the Convolutional Neural Network (CNN) algorithm, which is the basic method of deep learning. This study aims to facilitate physicians in filling out drug data in e-prescribing applications using speech recognition. The data used in this study were obtained from the open source dataset provided by Google and collected independent datasets. From the results of experiments that have been carried out, the accuracy achieved with 40 epochs and 40 direct impressions with different words is 90%. Where words are successfully recognized 36 words out of 40 words

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