filmov
tv
Multilingual Speech Recognition Methods using Deep Learning and Cosine Similarity
![preview_player](https://i.ytimg.com/vi/5KjRV1ynz2M/sddefault.jpg)
Показать описание
Multilingual Speech Recognition Methods using Deep Learning and Cosine Similarity
Authors
P Deepak Reddy, Chirag Rudresh and Adithya A S, PES University, India
Abstract
The paper includes research on discovering new methods for multilingual speech recognition and comparing the effectiveness of the existing solutions with the proposed novelty approaches. The audio and textual multilingual dataset contains multilingual sentences where each sentence contains words from two different languages - English and Kannada.
Keywords
Natural Language Processing, Deep Learning, Multilingual Speech Recognition, Machine Learning, Speech to Text.
#NaturalLanguageProcessing #DeepLearning #MultilingualSpeechRecognition #MachineLearning #SpeechtoText
00:00 Objective
00:17 Outline
00:40 Problem Statement
01:47 Project Scope
02:52 Research on Existing Solutions
04:27 Design Description
07:27 Modules and Implementation Details
22:12 Test Plan and Strategy
23:32 Results and Discussion
25:32 Conclusion
Authors
P Deepak Reddy, Chirag Rudresh and Adithya A S, PES University, India
Abstract
The paper includes research on discovering new methods for multilingual speech recognition and comparing the effectiveness of the existing solutions with the proposed novelty approaches. The audio and textual multilingual dataset contains multilingual sentences where each sentence contains words from two different languages - English and Kannada.
Keywords
Natural Language Processing, Deep Learning, Multilingual Speech Recognition, Machine Learning, Speech to Text.
#NaturalLanguageProcessing #DeepLearning #MultilingualSpeechRecognition #MachineLearning #SpeechtoText
00:00 Objective
00:17 Outline
00:40 Problem Statement
01:47 Project Scope
02:52 Research on Existing Solutions
04:27 Design Description
07:27 Modules and Implementation Details
22:12 Test Plan and Strategy
23:32 Results and Discussion
25:32 Conclusion