Patent Document ID: 9799331
Application ID: 15074579

Base Claim:
1. A feature compensation apparatus for speech recognition in a noisy environment, the feature compensation apparatus comprising a computer readable medium storing computer readable code that implements: a feature extractor configured to extract corrupt speech features from a corrupt speech signal with additive noise that consists of two or more frames; a noise estimator configured to estimate noise features based on the extracted corrupt speech features and compensated speech features; a linear model generator configured to approximate a Gaussian mixture model (GMM) probability distribution, the estimated noise features and the extracted corrupt speech features into a linear model; a probability calculator configured to calculate a correlation between adjacent frames of the corrupt speech signal; and a speech feature compensator configured to generate the compensated speech features by eliminating noise features of the extracted corrupt speech features while taking into consideration the correlation between adjacent frames of the corrupt speech signal and the estimated noise features, and to transmit the generated compensated speech features to the noise estimator, wherein the noise estimator estimates an average and variance of the noise features based on a dynamics model of noise features of the extracted corrupt speech features and a nonlinear observation model of corrupt speech features, and wherein the noise estimator reduces a Kalman gain of the average and variance of noise features that are to be updated in inverse proportion to a ratio of the extracted corrupt speech feature to the noise feature.

---

Claim 2:
2. The feature compensation apparatus of claim 1 , wherein the probability calculator comprises a probability distribution obtainer configured to obtain a GMM probability distribution of training speech features from training speech signals that consist of two or more frames, a transition probability codebook obtainer configured to obtain a transition probability of a GMM mixture component between adjacent frames of the training speech features, and a transition probability calculator configured to search transition probabilities of a GMM mixture component between adjacent frames of each of the training speech signals to calculate a transition probability of the GMM mixture component that corresponds to a transition probability of a mixture component between adjacent frames of the corrupt speech features extracted from the corrupt speech signal.