You have an AI 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.
To control smart devices using verbal commands, the solution primarily relies on speech-to-text conversion and language modeling. Speech-to-text is necessary to convert the spoken commands into written text so the system can process them. Language modeling is then used to understand the intent behind the textual commands and generate the appropriate actions based on this understanding. Key phrase extraction, while useful for identifying main concepts in text, doesn't provide the comprehensive understanding required for executing commands based on user intent.
This should be speech-to-text and language modeling. You need to use language modeling to determine the intent of the utterance and to perform an action based on that intent.
For me the answer is Speech to Text and Language Modeling
The answer is correct. We all agree speech-to-text is correct. Key Phrase Detection is also correct. Here is why: Key phrase extraction is a technique that identifies the most important phrases in a given text. eg: "Turn the light on", or "What is today's date?" Language modeling is a technique that is used to predict the probability of a sequence of words in a given language. It is used to generate text that is similar to the input text. For example, given the text “The cat sat on the”, a language model would predict that the next word is “mat” with a higher probability than “car”. Since we are talking about a smart device, the answer will be key phrase detection. Upvote me it makes sense to you.
I disagree. According to Microsoft, key phrase extraction lists the main concepts from unstructured text. In this case there is no unstructured text. https://learn.microsoft.com/en-us/azure/search/cognitive-search-skill-keyphrases
Of course there is: extracted from speech-to-text.
C. Speech to text D. Natural language understanding (NLU)
Considering the scenario described where the goal is to control smart devices using voice commands, the most appropriate choice would be to use speech-to-text conversion as the first step in the process and then apply language modeling to generate consistent and meaningful responses or actions based on the commands recognized in the produced text. This could allow the AI to understand the users' intent and respond appropriately. Key phrase extraction could also work but is more complex because an additional layer would have to be added that understands user intent based on the combination of keywords extracted. But it would become complex and probably less efficient as well. Language modeling solves this problem natively.
it should be C-D... as per Language modeling: Identify key terms and phrases, understand sentiments, and build conversational interfaces into applications.
I believe this should be speech-to-text and Language Modeling.
Language modeling is too broad of a term to apply here. Though this is part of the process, the system is actually looking for phrases to help it understand what the user wants it to do here. "Turn on", "lights", "close garage door" all require the AI to extract those phrases from what the user is saying, which is key phrase detection. You also need speech-to-text to translate the user's spoken words into text the language model can understand. It's more specific and better answers the question.
It should ne AD: Certainly! The AI solution for controlling smart devices via verbal commands utilizes two key types of Natural Language Processing (NLP) workloads: Text-to-Speech (TTS): TTS is a critical component that converts written text into spoken language. It enables the system to communicate with users by generating human-like speech from textual input1. Speech-to-Text (STT): STT, also known as Automatic Speech Recognition (ASR), performs the opposite function. It transcribes spoken language into written text, allowing the system to understand and process verbal commands2
These two types of NLP workloads are key phrase extraction and speech-to-text. Key phrase extraction is used to quickly identify the main concepts in text while speech-to-text is used to convert spoken words into written text. The key here is control smart devices, not a human conversation.
While it’s true that C. Speech-to-Text is used to interpret what is spoken and convert it into text, and B. Key Phrase Extraction can process the text to identify the main points, these two alone might not be sufficient for a complete AI solution that controls smart devices using verbal commands. it doesn’t necessarily understand the context or the specific actions that need to be taken based on those key phrases. For example, in a command like “Turn on the living room lights”, key phrase extraction might identify “turn on”, “living room”, and “lights” as key phrases, but it doesn’t inherently understand that “turn on” is an action that needs to be applied to the “living room lights”. That's my reason why language modeling is a better answer than key phrase extraction.
STT plus key phrase extraction perfectly fit the job.
I believe the given answer is correct. We dont need chat GPT like functionality here. We just need to know what action needs to be performed on what device. Key phrase extraction is perfect for the job.
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Speech-to-Text is OK Why Language Modeling is not OK ? Language models analyze bodies of text data to provide a basis for their word predictions. They are used in natural language processing (NLP) applications, particularly ones that generate text as an output. Some of these applications include , machine translation and question answering.