According to an embodiment, a detection apparatus detects a partial series similar to a search pattern from a parameter series including a sequence of parameters. The apparatus includes a local score acquirer, a difference score calculator, an accumulative score calculator, and a determiner. The local score acquirer is configured to acquire a local score representing a likelihood of the parameter in the search pattern for each of the parameters. The difference score calculator is configured to calculate a difference score by subtracting a threshold from the local score for each of the parameters. The accumulative score calculator is configured to calculate an accumulative score by accumulating the difference scores. The determiner is configured to compare the accumulative score with a reference value in size to determine whether the partial series is similar to the search pattern.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A detection apparatus that detects a partial series similar to a search pattern from a parameter series including a sequence of parameters, the apparatus comprising: a local score acquirer configured to acquire a local score representing a likelihood of the parameter in the search pattern for each of the parameters; a difference score calculator configured to calculate a difference score by subtracting a threshold from the local score for each of the parameters; an accumulative score calculator configured to calculate an accumulative score by accumulating the difference scores; and a determiner configured to compare the accumulative score with a reference value in size to determine whether the partial series is similar to the search pattern. 2. The apparatus according to claim 1, wherein the threshold is a boundary value of a value obtained by averaging the local scores of the respective parameters included in the partial series. 3. The apparatus according to claim 2, wherein the reference value is zero. 4. The apparatus according to claim 3, wherein the search pattern is a directed graph in which parameters and output probabilities of the parameters are allocated to states. 5. The apparatus according to claim 4, wherein the search pattern is a hidden Markov model in which parameters and output probabilities of the parameters are allocated to states, the local score acquirer acquires the output probability corresponding to each of the parameters included in the partial series for each of the states in the hidden Markov model, and outputs the acquired output probability as the local score of the parameter at the state, the difference score calculator calculates a subtraction score by subtracting the threshold from the local score corresponding to each of the parameters included in the partial series for each of the states in the hidden Markov model, and the accumulative score calculator calculates the accumulative score by accumulating the difference scores of the parameters coinciding with the partial series for each path in the hidden Markov model. 6. The apparatus according to claim 4, wherein the accumulative score calculator sequentially selects the parameters included in the parameter series one by one from the head of the parameter series and calculates the maximum accumulative score for each selected parameter on the basis of a dynamic programming. 7. The apparatus according to claim 6, wherein the search pattern is a left-to-right hidden Markov model, and the accumulative score calculator calculates the maximum accumulative score on the basis of the following recurrence formula: g  ( 0 , j ) = - ∞   ( 0 < j ≤ N )  g  ( τ , 0 ) = 0   ( 0 ≤ τ ≤ T ) g  ( τ , j ) = max  ( g  ( τ - 1 , j - 1 ) , g  ( τ - 1 , j ) ) + score  ( τ , j ) - THRESHOLD   ( 0 < τ ≤ T , 0 < j ≤ N ) where τ is a variable that represents an index of the parameter and is an integer larger than zero and equal to or smaller than T, T is an index of the end of the parameter series, j is a variable that represents a state number of the state in the hidden Markov model and is an integer larger than zero and equal to or smaller than N, g (τ,j) represents an evaluation value of the parameter at the index τ at a state with the state number j, score(τ, j) represents the local score of the parameter at the index τ, THRESHOLD represents the threshold, and max(g(τ−1, j−1), g(τ−1, j)) represents a function that selects the larger one of g(τ−1, j−1) and g(τ−1, j). 8. The apparatus according to claim 7, wherein the determiner determines whether g(τ, N) is larger than zero for each selected parameter. 9. The apparatus according to claim 1, wherein the parameters represent an acoustic feature of a voice. 10. The apparatus according to claim 8, further comprising an analyzer configured to convert a voice signal into the parameter series. 11. The apparatus according to claim 10, further comprising: an input unit configured to receive a character string; a model storage configured to store therein an acoustic model in which a phoneme and a hidden Markov model are in association with each other; and a search pattern generator configured to generate a hidden Markov model corresponding to the character string as the search pattern on the basis of the acoustic model. 12. A method for detecting a partial series similar to a search pattern from a parameter series including a sequence of parameters, the method comprising: acquiring a local score representing a likelihood of the parameter in the search pattern for each of the parameters; calculating a difference score by subtracting a threshold from the local score for each of the parameters; calculating an accumulative score by accumulating the difference scores; and comparing the accumulative score with a reference value in size and determining whether the partial series is similar to the search pattern. 13. A computer program product comprising a computer-readable medium containing a program for detecting a partial series similar to a search pattern from a parameter series including a sequence of parameters, the program causing a computer to execute: acquiring a local score representing a likelihood of the parameter in the search pattern for each of the parameters; calculating a difference score by subtracting a threshold from the local score for each of the parameters; calculating an accumulative score by accumulating the difference scores; and comparing the accumulative score with a reference value in size and determining whether the partial series is similar to the search pattern.