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Kernel eigenvoice

Kernel eigenvoice is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Kernel eigenvoice rather than just read about it. In short: Speaker adaptation is an important technology to fine-tune either features or speech models for mis-match due to inter-speaker variation. In the last decade, eigenvoice (EV) speaker adaptation has been developed.

Key takeaways

  • Kernel eigenvoice belongs to computer science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Kernel eigenvoice to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Kernel eigenvoice from memory before moving on to harder problems.

Reference excerpt

Speaker adaptation is an important technology to fine-tune either features or speech models for mis-match due to inter-speaker variation. In the last decade, eigenvoice (EV) speaker adaptation has been developed. It makes use of the prior knowledge of training speakers to provide a fast adaptation algorithm (in other words, only a small amount of adaptation data is needed). Inspired by the kernel eigenface idea in face recognition, kernel eigenvoice (KEV) is proposed. KEV is a non-linear generalization to EV. This incorporates Kernel principal component analysis, a non-linear version of Principal Component Analysis, to capture higher order correlations in order to further explore the speaker space and enhance recognition performance.

See also fMLLR

References

External links Kernel Eigenvoice Speaker Adaptation Archived 2012-03-12 at the Wayback Machine, ScientificCommons Mak, B.; Ho, S. (2005). "Various Reference Speakers Determination Methods for Embedded Kernel Eigenvoice Speaker Adaptation". IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005. Proceedings. ICASSP '05. Vol. 1. pp. 981–984. doi:10.1109/ICASSP.2005.1415280. Mak, B.; Kwok, J. T.; Ho, S. (September 2005). "Kernel Eigenvoice Speaker Adaptation". IEEE Transactions on Speech and Audio Processing. 13 (5): 984–992. doi:10.1109/TSA.2005.851971. ISSN 1063-6676. S2CID 7361772. Retrieved 2017-11-15. Speedup of Kernel Eigenvoice Speaker Adaptation by Embedded Kernel PCA, ICSLP 2004. Speaker Adaptation via Composite Kernel PCA, NIPS 2003. Mak, Brian Kan-Wing; Hsiao, Roger Wend-Huu; Ho, Simon Ka-Lung; Kwok, J. T. (July 2006). "Embedded kernel eigenvoice speaker adaptation and its implication to reference speaker weighting". IEEE Transactions on Audio, Speech, and Language Processing. 14 (4): 1267–1280. CiteSeerX 10.1.1.206.4596. doi:10.1109/TSA.2005.860836. S2CID 7527119. {{cite journal}}: Cite uses deprecated parameter |citeseerx= (help)

Worked examples

Example 1 — a first encounter with Kernel eigenvoice

Start with the simplest possible case. Write down what Kernel eigenvoice claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Kernel eigenvoice before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Kernel eigenvoice ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Kernel eigenvoice

In research
Kernel eigenvoice appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Kernel eigenvoice in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Kernel eigenvoice is common in secondary-school and first-year university syllabi. It links to neighbouring topics Kernel methods for machine learning, so understanding it makes those chapters shorter.
In everyday life
Look for Kernel eigenvoice outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.
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How to study Kernel eigenvoice in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Kernel eigenvoice means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Kernel eigenvoice out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Kernel eigenvoice in simple terms?

Speaker adaptation is an important technology to fine-tune either features or speech models for mis-match due to inter-speaker variation. In the last decade, eigenvoice (EV) speaker adaptation has been developed.

Why does Kernel eigenvoice matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Kernel eigenvoice?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Kernel eigenvoice.

Tags

  • Kernel methods for machine learning

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