According to an embodiment, a training device trains a neural network that outputs a posterior probability that an input signal belongs to a particular class. An output layer of the neural network includes N units respectively corresponding to classes and one additional unit. The training device includes a propagator, a probability calculator, and an updater. The propagator supplies a sample signal to the neural network and acquires (N+1) input values for each unit at the output layer. The probability calculator supplies the input values to a function to generate a probability vector including (N+1) probability values respectively corresponding to the units at the output layer. The updater updates a parameter included in the neural network in such a manner to reduce an error between a teacher vector including (N+1) target values and the probability vector. A target value corresponding to the additional unit is a predetermined constant value.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A training device configured to train a neural network that outputs a posterior probability that an input signal belongs to a particular class, an output layer of the neural network including N units respectively corresponding to classes and one additional unit, N being an integer of 2 or larger, the device comprising: a propagator configured to supply a sample signal to the neural network, and to acquire, for each of the units at the output layer, (N+1) input values that are obtained by connecting signals output from a layer immediately preceding the output layer according to a set parameter; a probability calculator configured to supply the input values to a function for calculating the posterior probability to generate a probability vector including (N+1) probability values respectively corresponding to the units at the output layer; and an updater configured to update the parameter included in the neural network in such a manner to reduce an error between a teacher vector and the probability vector, the teacher vector including (N+1) target values respectively corresponding to the units at the output layer, wherein a target value corresponding to the additional unit is a predetermined constant value. 2. The device according to claim 1, wherein the teacher vector is such that target values respectively corresponding to classes each are a value based on a probability that the sample signal belongs to the class. 3. The device according to claim 2, wherein the teacher vector is such that each of the target values is 0 or larger, a sum of all the target values is 1, and the target value corresponding to the additional unit is ½. 4. The device according to claim 3, wherein the teacher vector is such that a target value corresponding to a correct answer class is ½, the target value corresponding to the additional unit is ½, and the other target values are 0. 5. The device according to claim 1, wherein the propagator is configured to propagate the sample signal in a forward direction from an input layer of the neural network, and to acquire the (N+1) input values. 6. The device according to claim 5, wherein the updater is configured to propagate the error in a reverse direction from the output layer of the neural network to update parameters at respective layers so as to reduce the error. 7. The device according to claim 1, further comprising an error calculator configured to calculate the error between the teacher vector and the probability vector, the teacher vector including the (N+1) target values respectively corresponding to the units at the output layer. 8. The device according to claim 7, wherein the probability calculator is configured to supply the input values to a softmax function to generate the probability vector including the (N+1) probability values. 9. The device according to claim 8, wherein the error calculator is configured to calculate a cross-entropy error. 10. The device according to claim 9, wherein each of the units at the output layer of the neural network calculates a probability value of the posterior probability that the input signal belongs to a class corresponding to the unit, using a function to subtract the input value corresponding to the additional unit from the input value corresponding to the unit. 11. A speech detection device configured to detect, from a speech signal, a part of the speech signal similar to a specified search pattern, the device comprising: an analyzer configured to covert the speech signal into a speech parameter sequence; a score calculator configured to use the neural network to calculate a score representing likelihood of occurrence of each speech parameter included in the speech parameter sequence in the search pattern; a score calculator configured to use a neural network to calculate a score representing likelihood of occurrence of each speech parameter included in the speech parameter sequence in the search pattern; a detector configured to detect, based on the score, whether the speech signal includes the search pattern; and the training device configured to train the neural network according to claim 1. 12. A training method for training a neural network that outputs a posterior probability that an input signal belongs to a particular class, an output layer of the neural network including N units respectively corresponding to classes and one additional unit, N being an integer of 2 or larger, the method comprising: supplying a sample signal to the neural network; acquiring, for each of the units at the output layer, (N+1) input values that are obtained by connecting signals output from a layer immediately preceding the output layer according to a set parameter; supplying the input values to a function for calculating the posterior probability to generate a probability vector including (N+1) probability values respectively corresponding to the units at the output layer; and updating the parameter included in the neural network in such a manner to reduce an error between a teacher vector and the probability vector, the teacher vector including (N+1) target values respectively corresponding to the units at the output layer, wherein a target value corresponding to the additional unit is a predetermined constant value. 13. A computer program product comprising a computer readable medium including a computer program causing an information processing device to function as a training device configured to train a neural network that outputs a posterior probability that an input signal belongs to a particular class, an output layer of the neural network including N units respectively corresponding to classes and one additional unit, N being an integer of 2 or larger, the computer program causing the training device to function as: a propagator configured to supply a sample signal to the neural network, and to acquire, for each of the units at the output layer, (N+1) input values that are obtained by connecting signals output from a layer immediately preceding the output layer according to a set parameter; a probability calculator configured to supply the input values to a function for calculating the posterior probability to generate a probability vector including (N+1) probability values respectively corresponding to the units at the output layer; and an updater configured to update the parameter included in the neural network in such a manner to reduce an error between a teacher vector and the probability vector, the teacher vector including (N+1) target values respectively corresponding to the units at the output layer, wherein a target value corresponding to the additional unit is a predetermined constant value.