Speech Recognition and AI: Natural Language Processing in Action MCQs
Questions: 30
Questions
-
1. What is speech recognition?
- a) The ability of a machine to recognize and understand spoken words.
- b) The ability of a machine to generate human-like speech.
- c) The ability of a machine to identify visual cues in speech.
- d) The ability of a machine to perform linguistic analysis of text.
-
2. Which of the following is a popular framework used for NLP tasks?
- a) TensorFlow
- b) PyTorch
- c) NLTK
- d) OpenCV
-
3. What does NLP stand for in the context of AI?
- a) Neural Language Processing
- b) Natural Language Programming
- c) Natural Language Processing
- d) Neural Linguistic Programming
-
4. Which of the following is an application of speech recognition?
- a) Voice-controlled assistants like Siri and Alexa
- b) Machine translation
- c) Document summarization
- d) Named entity recognition
-
5. Which machine learning technique is widely used in NLP?
- a) Reinforcement Learning
- b) Deep Learning
- c) Supervised Learning
- d) Unsupervised Learning
-
6. What is tokenization in NLP?
- a) The process of converting text into numerical values.
- b) The process of splitting text into smaller components like words or phrases.
- c) The process of translating text into another language.
- d) The process of assigning meanings to words in a sentence.
-
7. Which of these algorithms is used for speech recognition?
- a) Hidden Markov Models
- b) Decision Trees
- c) Support Vector Machines
- d) K-Means Clustering
-
8. What does the 'Bag of Words' model represent in NLP?
- a) A sequence of words used in a sentence.
- b) A statistical model representing the frequency of words in a text without considering word order.
- c) A method of encoding words into numerical vectors.
- d) A grammar-based approach to understanding syntax.
-
9. Which of the following tasks is a typical use case for NLP?
- a) Text classification
- b) Facial recognition
- c) Object detection
- d) Speech synthesis
-
10. What is the purpose of stop words in NLP?
- a) They are words that add significant meaning to a sentence.
- b) They are words removed from text to simplify analysis.
- c) They are words that should always be included in any NLP model.
- d) They are keywords used for search engine optimization.
-
11. What is a phoneme in speech recognition?
- a) A unit of meaning in language.
- b) A part of a word's syntactic structure.
- c) A visual cue in speech.
- d) A unit of sound that can distinguish words in a language.
-
12. What does the term 'semantic analysis' refer to in NLP?
- a) Analyzing the structure of a sentence.
- b) Analyzing the frequency of words in a text.
- c) Identifying named entities in text.
- d) Extracting meaning from text.
-
13. Which neural network architecture is commonly used for speech recognition tasks?
- a) Convolutional Neural Networks (CNN)
- b) Recurrent Neural Networks (RNN)
- c) Generative Adversarial Networks (GAN)
- d) Radial Basis Function Networks (RBFN)
-
14. What is the primary function of a speech-to-text system?
- a) To translate written text into speech.
- b) To convert speech into written text.
- c) To process natural language syntax.
- d) To generate speech from a text prompt.
-
15. What is the function of part-of-speech tagging in NLP?
- a) Identifying the grammatical category of words in a sentence.
- b) Extracting the main subject and object from a sentence.
- c) Translating words from one language to another.
- d) Predicting the sentiment of the text.
-
16. What is the primary challenge in automatic speech recognition (ASR)?
- a) Understanding regional accents and dialects.
- b) Generating fluent speech.
- c) Understanding complex sentence structures.
- d) Summarizing spoken content.
-
17. Which of the following is an example of a text generation task in NLP?
- a) Summarization
- b) Speech synthesis
- c) Language modeling
- d) Part-of-speech tagging
-
18. Which approach is used to improve the accuracy of speech recognition systems?
- a) Data augmentation
- b) Overfitting
- c) Data removal
- d) Reducing the training set size
-
19. What is a key challenge in natural language generation (NLG)?
- a) Classifying text into categories
- b) Understanding sentiment
- c) Generating grammatically correct sentences
- d) Extracting named entities
-
20. What does the term 'intent recognition' refer to in NLP-based systems?
- a) Identifying the specific action or purpose behind a user's input.
- b) Generating text responses based on input.
- c) Translating text into another language.
- d) Recognizing the speaker's identity in speech.
-
21. What is the function of a voicebot?
- a) To interact with users through written text.
- b) To respond to users through spoken language.
- c) To classify text into categories.
- d) To generate visual representations from speech.
-
22. Which of these is a technique used to improve speech recognition performance?
- a) Acoustic modeling
- b) Sentiment analysis
- c) Named entity recognition
- d) Text summarization
-
23. What is the primary task of a speech synthesis system?
- a) To convert text into speech.
- b) To transcribe spoken words into text.
- c) To perform sentiment analysis on text.
- d) To generate machine translations.
-
24. Which is a common challenge faced by speech recognition systems?
- a) Accurately recognizing hand gestures
- b) Detecting faces in images
- c) Discriminating between different voices in audio
- d) Ambiguity in natural language processing
-
25. What does 'transfer learning' refer to in NLP models?
- a) Generating new text data from existing data.
- b) Transferring one model's data to another.
- c) Using a pre-trained model and fine-tuning it for a specific task.
- d) Learning to generate different languages from the same model.
-
26. What is a common application of speech recognition in healthcare?
- a) Voice-controlled robotic surgery.
- b) Medical transcription of patient records.
- c) Detecting anomalies in speech patterns.
- d) Identifying medical entities in text.
-
27. What is the goal of a language model in NLP?
- a) To predict the next word or sequence of words in a sentence.
- b) To translate words from one language to another.
- c) To extract names and places from text.
- d) To generate grammatically correct speech.
-
28. Which of the following is not an NLP task?
- a) Language translation
- b) Sentiment analysis
- c) Face recognition
- d) Text summarization
-
29. What type of learning is typically used in supervised speech recognition systems?
- a) Reinforcement Learning
- b) Unsupervised Learning
- c) Supervised Learning
- d) Semi-supervised Learning
-
30. What is the role of deep neural networks in NLP tasks?
- a) To capture complex patterns and relationships in data.
- b) To classify text into predefined categories.
- c) To break text into smaller components.
- d) To generate word embeddings.
Ready to put your knowledge to the test? Take this exam and evaluate your understanding of the subject.
Start Exam