Download Automatic Speech and Speaker Recognition: Large Margin and by Joseph Keshet, Samy Bengio PDF
By Joseph Keshet, Samy Bengio
This ebook discusses huge margin and kernel equipment for speech and speaker reputation
Speech and Speaker reputation: huge Margin and Kernel tools is a collation of study within the fresh advances in huge margin and kernel tools, as utilized to the sphere of speech and speaker popularity. It offers theoretical and useful foundations of those equipment, from aid vector machines to giant margin equipment for established studying. It additionally presents examples of enormous margin established acoustic modelling for non-stop speech recognizers, the place the grounds for sensible huge margin series studying are set. huge margin equipment for discriminative language modelling and textual content self sufficient speaker verification also are addressed during this ebook.
Key positive factors:
- Provides an up to date picture of the present country of study during this box
- Covers very important points of extending the binary aid vector desktop to speech and speaker attractiveness purposes
- Discusses huge margin and kernel strategy algorithms for series prediction required for acoustic modeling
- Reviews prior and current paintings on discriminative education of language versions, and describes assorted huge margin algorithms for the appliance of part-of-speech tagging
- Surveys contemporary paintings at the use of kernel ways to text-independent speaker verification, and introduces the most techniques and algorithms
- Surveys fresh paintings on kernel methods to studying a similarity matrix from info
This ebook should be of curiosity to researchers, practitioners, engineers, and scientists in speech processing and laptop studying fields.
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Additional info for Automatic Speech and Speaker Recognition: Large Margin and Kernel Methods
The weight vectors wr are also called prototypes. Occasionally the notation is abused and the collection W is thought of as a concatenation of the k-vectors w1 , . . , wk , that is W = (w1 , . . , wk ). In this case the norm of the collection W is defined as W 2 = wr 2 . r We define the hypothesis h parameterized by W to be h (x) = ((w1 · x) , . . , (wk · x)) ∈ Rk . 1) We denote by hr (x) = (wr · x). The model composed of the hypothesis class and the representation of input space is called Single-Instance Multi-Prototype.
Furthermore, if α is an optimizer of the dual problem then the following vector is an optimizer of the primal problem: w= 1 λm m αi yi φ(xi ). 17) i=1 The quantity f (w) − D(α) is called the duality gap and it upper bounds the suboptimality of w since f (w) − f (w ) ≤ f (w) − D(α). 4) is not a dual problem but rather α a different parametrization of the primal problem. AUTOMATIC SPEECH AND SPEAKER RECOGNITION 24 One advantage of the dual problem is that it can be easily decomposed. That is, we can choose a subset of the dual variables and optimize D(α) over these variables while keeping the rest of the dual variables intact.
The elements of the categories set C correspond to the nodes of the graph. Each ordered pair in the preference relation (c, d) ∈ y ⊂ C × C translates into a directed edge c → d of the graph. No self loops in a graph represent reflexive relations and no loops of size two represent anti-symmetry relations. We do not enforce transitivity (if there is a pass from node c to node d then there is an edge between these two nodes). We refer to this type of preference as semi-orders, the formal definition of which is now given.