Which AI solution enables speech recognition and voice response for a mobile app?

AIF-C01 By islever Editorial Last updated

Short answer

A Use a deep learning neural network to perform speech recognition.

Question

A company is building a mobile app for users who have a visual impairment. The app must be able to hear what users say and provide voice responses. Which solution will meet these requirements?
  1. Use a deep learning neural network to perform speech recognition. Correct Answer
  2. Build ML models to search for patterns in numeric data.
  3. Use generative AI summarization to generate human-like text.
  4. Build custom models for image classification and recognition.

Key Takeaways

The exam tests your ability to match AI use cases to the correct technology; the trap is choosing generative AI summarization, which can produce text but does not handle speech input.

For a mobile app that must hear user speech and provide voice responses, the correct AWS AIF-C01 answer is to use a deep learning neural network for speech recognition. Community consensus confirms this approach mirrors voice assistants like Alexa and Siri.

Common mistake: Choosing C (generative AI summarization) because it generates human-like text, but it does not convert spoken words to text or handle speech input, so it cannot satisfy the 'hear what users say' requirement.

Expert Analysis

Why the Answer Is Correct

A deep learning neural network is the core technology behind speech recognition systems. It converts spoken language into text, which can then be processed to generate a voice response, making option A the only choice that fully addresses both hearing and responding to users. Community comments note that this is similar to voice assistants like Amazon Alexa and Apple's Siri, which use deep learning for speech-to-text, processing, and text-to-speech. The comments also emphasize that option A directly handles the input of speech, unlike other options.

Why the Other Options Are Wrong

Option B (build ML models to search for patterns in numeric data) is irrelevant because speech is audio, not numeric tabular data, and does not enable voice responses. Option C (generative AI summarization) can create human-like text but does not process audio input, so it fails the 'hear what users say' requirement. Option D (image classification and recognition) is for visual data, not speech, and would not help visually impaired users communicate via voice.

Community Comment Notes

Comments unanimously support A. One commenter explicitly notes that while C sounds feasible, it does not handle the input of speeches. Another highlights the end-to-end architecture of voice assistants, reinforcing that deep learning covers both speech recognition and response generation. These insights align with the official AWS AIF-C01 objectives around identifying appropriate AI and ML solutions for different use cases.

Official Reference

These links open the official vendor documentation in a new tab.

Exam Strategy

When a question involves voice or speech input, immediately think of speech recognition (deep learning) and text-to-speech services like Amazon Transcribe and Amazon Polly. Eliminate options that address visual, numeric, or text-only generation, as they cannot process spoken input.

Community Votes

A
100%

100% of anonymous learners picked answer A. Votes are pick records left by other test-takers — they are not the verified answer.

Discussion 4 contributions

Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.

From public community threads 4

  1. chris_spencer 👍 2 Answered A
    A. Use a deep learning neural network to perform speech recognition. While C sounds feasible, it does not handle the input of the speeches
  2. kopper2019 👍 1 Answered A
    A. Use a deep learning neural network to perform speech recognition. This type of solution is similar to voice assistants like Amazon Alexa or Apple's Siri, which use deep learning for: Converting speech to text (speech recognition) Processing the request Converting response text back to speech (text-to-speech)
  3. Jessiii 👍 1 Answered A
    Deep learning neural networks are commonly used for speech recognition tasks, converting spoken language into text, which can then be processed to provide appropriate voice responses.
  4. ajey255 👍 1 Answered A
    A. Other options don't meet the requirement of speech recognition and voice response

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