Available either online or on-site, these instructor-led live Speech Recognition training courses provide a comprehensive understanding of both fundamental and advanced concepts through interactive, hands-on exercises.
Speech Recognition courses are offered in two formats: "online live training" and "onsite live training". Online live training, also referred to as "remote live training", is conducted via an interactive remote desktop session. Onsite live training can be delivered directly at your premises in Prague or within NobleProg's corporate training centers located in Prague.
Speech Recognition is also widely recognized as Voice Recognition, Speech Technology, or Speech to Text (STT).
From Prague Main Train Station (Praha hlavní nádraží)
Take tram 9 from Hlavní nádraží toward Sídliště Řepy.
Get off at Újezd.
Walk about 10 minutes toward Malostranské náměstí / Prokopská.
Continue along Prokopská to 296/8.
Alternative: Take the metro C from Hlavní nádraží → Muzeum, change to metro A → Malostranská, then walk across Malá Strana. This involves more walking.
From Prague Bus Station — Florenc
Take metro B from Florenc toward Zličín.
Get off at Můstek.
Change to metro A toward Nemocnice Motol.
Get off at Malostranská.
Walk approximately 10–15 minutes to Prokopská 296/8.
This instructor-led, live training in Prague (online or onsite) is designed for beginner to intermediate-level software developers and data scientists who wish to implement LLMs in speech recognition and synthesis systems.
By the end of this training, participants will be able to:
Understand the role of LLMs in speech technologies.
Implement LLMs for accurate speech recognition and natural-sounding speech synthesis.
Integrate LLMs with speech recognition engines and speech synthesizers.
Evaluate and improve the performance of speech systems using LLMs.
Stay informed about current trends and future directions in speech technologies.
This live training in Prague guides intermediate professionals in deploying and refining AI speech recognition. Learners will evaluate APIs, address multilingual hurdles, and develop transcription workflows. The curriculum encompasses model architecture, preprocessing, and practical lab exercises.
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