Features for speaker recognition

1. What are the features of speech recognition systems?
2. What is feature extraction in speaker recognition?
3. What are the four different ways to perform speaker recognition?
4. How do you identify a speaker?

What are the features of speech recognition systems?

Speech recognizers are made up of a few components, such as the speech input, feature extraction, feature vectors, a decoder, and a word output. The decoder leverages acoustic models, a pronunciation dictionary, and language models to determine the appropriate output.

What is feature extraction in speaker recognition?

Feature extraction is accomplished by changing the speech waveform to a form of parametric representation at a relatively lesser data rate for subsequent processing and analysis. This is usually called the front end signal-processing [9, 10].

What are the four different ways to perform speaker recognition?

Speaker recognition is a pattern recognition problem. The various technologies used to process and store voice prints include frequency estimation, hidden Markov models, Gaussian mixture models, pattern matching algorithms, neural networks, matrix representation, vector quantization and decision trees.

How do you identify a speaker?

In speaker identification, an utterance from an unknown speaker is analyzed and compared with speech models of known speakers. The unknown speaker is identified as the one whose model best matches the input utterance.