Exam AI-900 topic 1 question 122 discussion

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

Deepseek answer is (C & D). ChatGPT and Gimni answer is A & C. My answer is: B & C.

The question only said "by using verbal commands". That means the Key phrase extraction doesn't need to be used.

Not a huge fan of this question. It is missing an option that allows for intent recognition like Conversation language understanding or LUIS.

Completely agree with VintageLady explanation

answer is C D

Language modeling is based on prediction; it uses the preceding words in a sentence to determine context and "predict" the next word (e.g., autofill), so I don't think it's as useful here (your Alexa does not need to predict what you're asking, only to process the command you give and respond appropriately, so extracting key phrases in what you say to her is more important).

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.

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.

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

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.

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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.

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.