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Speech Recognition Over Digital Channels: Robustness and by Antonio Peinado, Jose Segura

By Antonio Peinado, Jose Segura

Книга Speech popularity Over electronic Channels: Robustness and criteria Speech reputation Over electronic Channels: Robustness and StandardsКниги Электротехника и связь Автор: Antonio Peinado , Jose Segura Год издания: 2006 Формат: pdf Издат.:Wiley Страниц: 274 Размер: 2,6 ISBN: 0470024003 Язык: Английский0 (голосов: zero) Оценка:Automatic speech acceptance (ASR) is a truly appealing ability for human-machine interplay. The measure of adulthood reached via speech popularity applied sciences in the course of contemporary years permits the advance of functions that use them. specifically, ASR exhibits a huge power in cellular environments, the place units comparable to cell phones or PDAs are used, and for web Protocol (IP) purposes. Speech reputation Over electronic Channels is the 1st e-book of its sort to supply a whole procedure comprehension, addressing the themes of dispensed and network-based speech acceptance concerns and criteria, the options of speech processing and transmission, and approach architectures and robustness. Describes different client/server architectures for distant speech acceptance platforms, through which the buyer transmits speech parameters via a electronic channel to a distant attractiveness server specializes in robustness opposed to either adversarial acoustic environments (in the front-end) and bit errors/packet loss Discusses 4 ETSI criteria for allotted speech acceptance; the certainty of the factors and the applied sciences in the back of them offers the required history for the comprehension of distant speech attractiveness applied sciences This ebook will attract a wide-ranging viewers: engineers utilizing speech popularity platforms, researchers interested in ASR platforms and people attracted to processing and transmitting speech equivalent to sign processing and communications groups. it's going to even be of curiosity to technical specialists requiring an realizing of popularity over cellular and IP networks, and postgraduate scholars engaged on strong speech processing.

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This modified VA is obtained with a minimal modification of the normal VA (substituting bj (xt ) by [bj (xt )]γt ). 92) is independent of the specific form of the observation probabilities bi (xt ), and is applicable to both discrete and continuous density HMMs. 71) with diagonal covariance matrices, it is also possible to apply a specific weight γj,t for each feature xt (j ) (j = 1, . . 94) c In case of uncertain values we can consider that x is a random variable and, therefore, P (c|x) is a random variable too.

In the case of an ASR system, this observation sequence can be obtained from a sequence of feature vectors X = x1 , . . , xT submitted to a VQ process. However, the VQ process involves a loss of information that can reduce the system performance. This problem can be solved by forcing the HMM to work directly with the vectors x = (x(1), . . , x(p)) of the continuous p-dimensional representation space Rp . 70) vk ∈V (si ) where each P (x|vk , si ) is a log-concave or an elliptically symmetric pdf and cik = P (vk |si ) is the pdf weight (the sum of the pdf weights over k must be 1).

N ; k = 1, . . 33) (i = 1, . . 34) These probabilities must verify M k=1 bi (vk ) = 1 5. A matrix of initial states, in which each element is the probability of having a certain state as initial state = {πi } with πi = P (q1 = si ) (i = 1, . . 36) As can be seen, the model is fully defined by matrices A, B y . Thus, from now on, the model will be noted as the set of parameters λ = (A, B, ). As previously mentioned, the model generates a sequence of observations O = (o1 , o2 , . . , oT ) and a hidden sequence of states Q = (q1 , q2 , .

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