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.