AI-900: Empowering Control: Unveiling Natural Language Processing in AI for Smart Devices

Discover the dynamic capabilities of an AI solution for smart device control through verbal commands. Delve into the intricacies of Natural Language Processing (NLP) workloads, including text-to-speech, language modeling, translation, and speech-to-text. Elevate your understanding of how these components enhance user interaction and device responsiveness.

Question

You have an Al solution that provides users with the ability to control smart devices by using verbal commands.

Which two types of natural language processing (NLP) workloads does the solution use? Each correct answer presents part of the solution.

NOTE: Each correct selection is worth one point.

A. text-to-speech
B. translation
C. language modeling
D. key phrase extraction
E. speech-to-text

Answer

A. text-to-speech
E. speech-to-text

Explanation

The correct answer is A and E.

A. text-to-speech

Text-to-speech is a type of natural language processing (NLP) workload that converts text into spoken audio. This is useful for providing users with audible feedback or instructions from the AI solution. For example, the AI solution can use text-to-speech to tell the user that the smart device has been turned on or off, or to confirm the user’s command.

E. speech-to-text

Speech-to-text is another type of natural language processing (NLP) workload that converts spoken audio into text. This is useful for understanding the user’s verbal commands and extracting the relevant information or intent from them. For example, the AI solution can use speech-to-text to transcribe the user’s voice and parse the text for keywords or phrases that indicate which smart device to control and how.

B. translation

Translation is not a type of natural language processing (NLP) workload that the AI solution uses. Translation converts text or speech from one language to another. This is useful for providing users with multilingual support or communication. However, the AI solution does not need to translate the user’s commands or the feedback from the smart devices, as they are assumed to be in the same language.

C. language modeling

Language modeling is not a type of natural language processing (NLP) workload that the AI solution uses. Language modeling is the process of creating a statistical model that can generate or predict words or sentences based on the previous or surrounding context. This is useful for generating natural language text or speech, such as captions, summaries, or dialogues. However, the AI solution does not need to generate any text or speech that is not directly related to the user’s commands or the feedback from the smart devices.

D. key phrase extraction

Key phrase extraction is not a type of natural language processing (NLP) workload that the AI solution uses. Key phrase extraction is the process of identifying and extracting the most important or relevant words or phrases from a text document. This is useful for summarizing the main topics or themes of a document, or for indexing and searching documents. However, the AI solution does not need to extract any key phrases from the user’s commands or the feedback from the smart devices, as it only needs to understand the basic information or intent from them.

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