Speech Recognition and AI: Natural Language Processing in Action MCQs

Explore key techniques in voice recognition, transcription and AI-powered language models. Ideal for students and AI professionals.

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1. What is speech recognition?

  • The ability of a machine to recognize and understand spoken words.
  • The ability of a machine to generate human-like speech.
  • The ability of a machine to identify visual cues in speech.
  • The ability of a machine to perform linguistic analysis of text.

2. Which of the following is a popular framework used for NLP tasks?

  • TensorFlow
  • PyTorch
  • NLTK
  • OpenCV

3. What does NLP stand for in the context of AI?

  • Neural Language Processing
  • Natural Language Programming
  • Natural Language Processing
  • Neural Linguistic Programming

4. Which of the following is an application of speech recognition?

  • Voice-controlled assistants like Siri and Alexa
  • Machine translation
  • Document summarization
  • Named entity recognition

5. Which machine learning technique is widely used in NLP?

  • Reinforcement Learning
  • Deep Learning
  • Supervised Learning
  • Unsupervised Learning

6. What is tokenization in NLP?

  • The process of converting text into numerical values.
  • The process of splitting text into smaller components like words or phrases.
  • The process of translating text into another language.
  • The process of assigning meanings to words in a sentence.

7. Which of these algorithms is used for speech recognition?

  • Hidden Markov Models
  • Decision Trees
  • Support Vector Machines
  • K-Means Clustering

8. What does the 'Bag of Words' model represent in NLP?

  • A sequence of words used in a sentence.
  • A statistical model representing the frequency of words in a text without considering word order.
  • A method of encoding words into numerical vectors.
  • A grammar-based approach to understanding syntax.

9. Which of the following tasks is a typical use case for NLP?

  • Text classification
  • Facial recognition
  • Object detection
  • Speech synthesis

10. What is the purpose of stop words in NLP?

  • They are words that add significant meaning to a sentence.
  • They are words removed from text to simplify analysis.
  • They are words that should always be included in any NLP model.
  • They are keywords used for search engine optimization.

11. What is a phoneme in speech recognition?

  • A unit of meaning in language.
  • A part of a word's syntactic structure.
  • A visual cue in speech.
  • A unit of sound that can distinguish words in a language.

12. What does the term 'semantic analysis' refer to in NLP?

  • Analyzing the structure of a sentence.
  • Analyzing the frequency of words in a text.
  • Identifying named entities in text.
  • Extracting meaning from text.

13. Which neural network architecture is commonly used for speech recognition tasks?

  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • Generative Adversarial Networks (GAN)
  • Radial Basis Function Networks (RBFN)

14. What is the primary function of a speech-to-text system?

  • To translate written text into speech.
  • To convert speech into written text.
  • To process natural language syntax.
  • To generate speech from a text prompt.

15. What is the function of part-of-speech tagging in NLP?

  • Identifying the grammatical category of words in a sentence.
  • Extracting the main subject and object from a sentence.
  • Translating words from one language to another.
  • Predicting the sentiment of the text.

16. What is the primary challenge in automatic speech recognition (ASR)?

  • Understanding regional accents and dialects.
  • Generating fluent speech.
  • Understanding complex sentence structures.
  • Summarizing spoken content.

17. Which of the following is an example of a text generation task in NLP?

  • Summarization
  • Speech synthesis
  • Language modeling
  • Part-of-speech tagging

18. Which approach is used to improve the accuracy of speech recognition systems?

  • Data augmentation
  • Overfitting
  • Data removal
  • Reducing the training set size

19. What is a key challenge in natural language generation (NLG)?

  • Classifying text into categories
  • Understanding sentiment
  • Generating grammatically correct sentences
  • Extracting named entities

20. What does the term 'intent recognition' refer to in NLP-based systems?

  • Identifying the specific action or purpose behind a user's input.
  • Generating text responses based on input.
  • Translating text into another language.
  • Recognizing the speaker's identity in speech.

21. What is the function of a voicebot?

  • To interact with users through written text.
  • To respond to users through spoken language.
  • To classify text into categories.
  • To generate visual representations from speech.

22. Which of these is a technique used to improve speech recognition performance?

  • Acoustic modeling
  • Sentiment analysis
  • Named entity recognition
  • Text summarization

23. What is the primary task of a speech synthesis system?

  • To convert text into speech.
  • To transcribe spoken words into text.
  • To perform sentiment analysis on text.
  • To generate machine translations.

24. Which is a common challenge faced by speech recognition systems?

  • Accurately recognizing hand gestures
  • Detecting faces in images
  • Discriminating between different voices in audio
  • Ambiguity in natural language processing

25. What does 'transfer learning' refer to in NLP models?

  • Generating new text data from existing data.
  • Transferring one model's data to another.
  • Using a pre-trained model and fine-tuning it for a specific task.
  • Learning to generate different languages from the same model.

26. What is a common application of speech recognition in healthcare?

  • Voice-controlled robotic surgery.
  • Medical transcription of patient records.
  • Detecting anomalies in speech patterns.
  • Identifying medical entities in text.

27. What is the goal of a language model in NLP?

  • To predict the next word or sequence of words in a sentence.
  • To translate words from one language to another.
  • To extract names and places from text.
  • To generate grammatically correct speech.

28. Which of the following is not an NLP task?

  • Language translation
  • Sentiment analysis
  • Face recognition
  • Text summarization

29. What type of learning is typically used in supervised speech recognition systems?

  • Reinforcement Learning
  • Unsupervised Learning
  • Supervised Learning
  • Semi-supervised Learning

30. What is the role of deep neural networks in NLP tasks?

  • To capture complex patterns and relationships in data.
  • To classify text into predefined categories.
  • To break text into smaller components.
  • To generate word embeddings.

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