Приклади вживання Speech recognition Англійська мовою та їх переклад на Українською
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developing tools with speech recognition that will listen to your pronunciation
there was less error in speech recognition.
natural language processing, and speech recognition.
natural language processing, and speech recognition.
HMM models are widely used in speech recognition, for translating a time series of spoken words into text.
In addition, a speech recognition device can be used in a wide variety of environments.
Until recently, various systems for translating languages, speech recognition, individuals and images required the availability of productive data centers for data processing
The robot uses a speech recognition technology from Google(parent company Google), and refined over time, becoming smarter.
started to develop speech recognition software in 2000.
is designed to help developers improve services such as speech recognition, or in this case sound detection.
who specialize in speech recognition technologies.
including speech recognition, medical and biological image processing,
who specialize in speech recognition technologies.
ABB has implemented a hybrid voice recognition solution which is a combination of grammar based speech recognition and a statistical speech model.
Amazon's Polly cloud-based TTS service supports 28 languages, and Microsoft's Azure speech recognition API supports over 75.
speech, waveforms,">have more recently been shown to produce excellent larger-scale speech recognition results.
Microsoft claims that running CNTK on GPU clusters on Azure allowed it to accelerate speech recognition training for Cortana by an order of magnitude.
including computer vision, speech recognition, machine translation,
the teams led by Taras Vintsiuk developed speech recognition systems having passed a long way from spoken dialogue systems based on the BESM to portable devices with voice control.
Ibm which has the best system for one of the standard speech recognition tasks for large recovery speech recognition, showed that even it's very highly tuned system that was getting 18.8 percent can be beaten by one of these deep neural networks.