Directions : Read the following passage and answer the questions. Voice or…
2023
Directions : Read the following passage and answer the questions.
Voice or speaker recognition refers to a machine or program's ability to interpret dictation or understand and execute spoken commands. This technology has become increasingly prominent with the _____________ of artificial intelligence (AI) and intelligent assistants like Amazon's Alexa and Apple's Siri. Offering hands free interactions, voice recognition systems enable users to make requests, set reminders, and perform various tasks simply by speaking.
The process involves automatic speech recognition (ASR) programs that can identify and differentiate voices. Some ASR programs necessitate users to train the system for improved accuracy in converting speech to text. Evaluation of a voice's frequency, accent, and speech flow is integral to voice recognition systems.
While voice recognition and speech recognition are often used interchangeably, they have distinct meanings. Voice recognition identifies the speaker, whereas speech recognition assesses the content of what is said.
In practice, voice recognition software on computers converts analog audio into digital signals through analog-to-digital (A/D) conversion. This digital database of words or syllables is then compared to signals during pattern recognition. The size of the program's effective vocabulary depends on the computer's RAM capacity, with faster processing speeds enhancing search capabilities.
Voice recognition involves analyzing speech through models like the hidden Markov model or recurrent neural networks. The former breaks down spoken words into phonemes, while the latter uses previous outputs to influence current inputs, improving capabilities and accuracy with increased data.
The integration of voice recognition into smartphones and home devices like Google Home and Amazon Echo has made this technology ubiquitous. As more users engage with voice recognition, the wealth of data generated enhances the capabilities and accuracy of these systems, indicating a promising trajectory for the technology's future.
How does Markov model contribute to the overall improvement of voice recognition capabilities?
- A.
Incorporating and neglecting the preceding output to impact the input of the subsequent stage in voice recognition.
- B.
Utilizing phonemes for coarse identification of the origin of words and their association with the correct meaning.
- C.
Aggregating words into components to generate the output for the voice recognition process.
- D.
Dissection of words into phonetic components allows for a more granular analysis of voice recognition.
- E.
None of these
Attempted by 16 students.
Show answer & explanation
Correct answer: D
Concept
In a reading-comprehension question, the answer must come from what the passage explicitly states, not from outside knowledge. The method is to locate the exact sentence that names the entity in the question (here, the Markov model) and read precisely what role it is assigned.
Application
The passage states that voice recognition is analysed through models like the hidden Markov model or recurrent neural networks, and that 'the former breaks down spoken words into phonemes, while the latter uses previous outputs to influence current inputs.' The former refers to the Markov model. So, by the passage, the Markov model's contribution is to break spoken words down into their phonetic units (phonemes), enabling a finer, more granular analysis of speech.
Why the other options do not fit
“Incorporating and neglecting the preceding output to impact the input of the subsequent stage” describes the recurrent neural network (the latter), not the Markov model.
“Utilizing phonemes for coarse identification of the origin of words” misstates the purpose as locating word origins, which the passage never claims.
“Aggregating words into components to generate the output” reverses the action: the model breaks words down, it does not aggregate them.
Therefore the statement that matches the passage is that the Markov model dissects words into phonetic components for a more granular analysis.