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Hi ,
in this video we try to explain how we can implement speech to text recognition using deep learning from base using python . Speech to text recognition is a technology which is booming in the industry. It is being used in so many applications. we thought we should make video on it . it would be very helpful for you . you can comment the topic we will be making videos on it.
The primary advantage of speech recognition is searchability. Speech recognition is an interdisciplinary subject of computer science and computational linguistics that develops approaches and technology to allow the recognition and translation of spoken language into text by computers. It is often referred to as speech to text, computer voice recognition, or automated speech recognition (ASR) (STT). It draws on expertise and research from the domains of computer science, linguistics, and computer engineering. Speech synthesis is the opposite process.
A speaker must "train" (also known as "enrol") some voice recognition systems by reading text or a small vocabulary to the device. The accuracy of the speech recognition is improved by the system's analysis of the individual's voice and utilisation of that information. Systems without training are referred to as "speaker-independent" systems. Training-based systems are referred to be "speaker dependent".
Applications for speech recognition include voice user interfaces like voice dialling (for example, "call home"), call routing (for example, "I would like to make a collect call"), domotic appliance control, search key words (for example, "find a podcast where particular words were spoken"), simple data entry (for example, "enter a credit card number"), preparation of structured documents (for example, a radiology report), identification of speaker characteristics, and speech-to-text processing (for example, word (usually termed direct voice input).
Identifying the speaker, as opposed to what they are saying, is what voice recognition or speaker identification refers to. In systems that have been trained on a particular person's voice, interpreting speech can be made simpler by being able to identify the speaker. It can also be used to authenticate or verify a speaker's identity as part of a security procedure.
From a technological standpoint, speech recognition has a lengthy history and has seen several significant technological advancements. Recent developments in deep learning and big data have improved the field. Not only have there been an increase in academic papers published in the topic, but more significantly, the global industry has adopted a number of deep learning techniques for creating and implementing voice recognition systems.
following steps to build speech to text recognition model
1. What is speech to text recognition?
2. Why we need speech to text recognition pajama?
3 . What are the application of speech to text recognition?
4 . How to connect data set for speech to text recognition?
5. How to access data for preprocessing?
6. Free process both their Signals and test text.
7. How to build CTC layer for speech to text recognition?
8. How to build a deep learning model just like deepspeech 2?
9. How to train deep learning model that is RNN for the prepared data set ?
10. Use trained model for predicting on user interested wave files.
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