Woohyung Lim

Feature compensation based on switching linear dynamic model

In this letter, we propose a novel approach to feature compensation for robust speech recognition in noisy environments. We employ the switching linear dynamic model (SLDM) as a parametric model for the clean speech distribution, which enables us to exploit temporal correlations inherent in speech signals. Both the background noise and clean speech components are simultaneously estimated by means of the interacting multiple model (IMM) algorithm.

Cepstral domain feature compensation based on diagonal approximation

In this paper, we propose a novel approach to feature compensation performed in the cepstral domain. We apply the linear approximation method in the cepstral domain to simplify the relationship among clean speech, noise and noisy speech. Conventional log-spectral domain feature compensation methods usually assume that each log-spectral coefficient is independent, which is far from real observations. Processing in the cepstral domain has the advantage that the spectral correlation among different frequencies are taken into consideration. By using the diagonal covariance approximation, we can easily modify the conventional log-spectral domain feature compensation technique to fit to the cepstral domain. The proposed approach shows significant improvements in the AURORA2 speech recognition task.

An approach to robust unsupervised speaker adaptation

In this letter, we propose an approach to robust unsupervised speaker adaptation. Usually, recognition errors made on the adaptation utterances mislead parameter estimation when a speaker adaptation algorithm is operated in an unsupervised mode. In order to alleviate this problem, we first adapt a Gaussian mixture model (GMM) and then transform the hidden Markov model (HMM) parameters according to the information extracted from GMM adaptation.

Feature Compensation using More Accurate Statistics of Modeling Error

In this paper, we propose a novel approach to feature compensation for robust speech recognition in noisy environments. We analyze the statistics of the modeling error in the log mel magnitude spectrum domain, and model it as a Gaussian distribution. The mean and variance of the distribution are Gaussian functions of the SNR, which enables us to use the SNR dependency of the modeling error efficiently. The proposed feature compensation approach, which is based on the interacting multiple model (IMM) technique, incorporates the statistics of the modeling error and shows significant improvement in the AURORA2 speech recognition task.

Feature Compensation Incorporating Modeling Error Statistics

In this letter, we propose a novel approach to feature compensation for robust speech recognition in noisy environments. We analyze the error distribution of speech corruption model in the log spectral domain and represent the statistics as functions with respect to the signal-to-noise ratio. The proposed algorithm incorporates modeling error statistics into the interacting multiple model technique and shows a performance improvement over the AURORA2 speech recognition task.