Speech Translation (AI ,ML)

The Machines which can act, behave, and make decisions like humans are termed as Artificially Intelligent Machine. Machine Learning is a subset of Artificial Intelligence. We use various machine learning algorithms to make Artificially Intelligent machines that we use in our day to day life like Amazon Alexa, Youtube Recommendations, Google Translator, Face Unlock System in mobile phones.

In this post, we will cover everything you must know about the Speech Translation System in Artificial Intelligence.

Overview Of Speech Translation

Speech Translation is the process of converting the spoken conversational words from one language to another. It will help to build communication between people who speak different languages.

Speech Translator is been widely used in many industries like medical facilities, schools, police, hotels, retail stores, and factories. One of the very common examples of Speech translation is Google speech-to-speech.

Check out: Overview of Azure Machine Learning Service

How Speech Translation Works

Speech translation requires the integration of three software technologies.

Note : Do Read Our Blog on Automated Machine Learning.

1) Automatic Speech Recognition: It will help in converting the spoken words & phrases into the text in the same language.

2) Machine Translation: It will help in converting the text into a second language. It will replace each word in the text with the appropriate word in the second language.

3) Speech Synthesis: It will estimate the pronunciation of the text generated by machine translation and generate the speech in the second and desired language.

Note: To see a demo how actually speech to speech translation works click here.

Speech Service In Azure

The speech service of Azure includes the following Application programming interface (APIs).

1. Speech to Text: Transforming generated speech into Text form.
2. Text to Speech: Transforming text into the desired language speech.
3. Speech Translation: Translate speech from one language to speak in another language.

Note: Azure Supports translation of speech into 60 different languages.

Note: Speech Translation is covered in the Microsoft Azure AI Fundamentals Certification program (AI-900). Check out our blog to know more about this certification.

Steps To Create An Speech-To-Speech Translation Model

1) Data Collection: Collecting data (speech) from various sources for training the Model.

2) Speech Recognition: This phase is the most complicated and expensive part of the model creation. It will translate the spoken phrases into text in the same language.

3) Translation: Translating the text generated in the previous step into the text in the desired language.

4) Synthesis: Analysing the target text and pronunciation for the resulting output.

5) System Integration: Integrating all these steps to create an optimized model for speech translation.

6) Performance: Check the performance of the created model by testing them on some sample speech data.

Also check: All you need know about DP 100 Exam

Related/References:

• [DP-100] Microsoft Certified Azure Data Scientist Associate: Everything you must know
• [AI-900] Microsoft Certified Azure AI Fundamentals Course: Everything you must know
• Object Detection & Tracking in Azure Machine Learning
• Automated Machine Learning | Azure | Pros & Cons
• Microsoft Certified Azure Data Scientist Associate | DP 100 | Step By Step Activity Guides (Hands-On Labs)
• Azure Free Account: Steps to Register for Free Trial Account
• DP 100 Exam | Microsoft Certified Azure Data Scientist Associate
• [DP-100] Designing and Implementing a Data Science Solution on Azure
• Microsoft Azure Data Scientist DP-100 FAQ
• Datastores And Datasets In Azure
• Overview of Hyperparameter Tuning In Azure

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