Unsupervised equalization of Lombard effect for speech recognition in noisy adverse environment

When exposed to environmental noise, speakers adjust their speech production to maintain intelligible communication. This phenomenon, called Lombard effect (LE), is known to considerably impact the performance of automatic speech recognition (ASR) systems. In this study, novel frequency and cepstral domain equalizations that reduce the impact of LE on ASR are proposed. Short-time spectra of LE speech are transformed towards neutral ASR models in a maximum likelihood fashion. Dynamics of cepstral coefficients are normalized to a constant range using quantile estimations. The algorithms are incorporated in a recognizer employing a codebook of noisy acoustic models. In a recognition task on connected Czech digits presented in various levels of background car noise, the resulting system provides an absolute reduction in word error rate (WER) on 10 dB SNR data of 8.7% and 37.7% for female neutral and LE speech, and of 8.7% and 32.8% for male neutral and LE speech when compared to the baseline system employing perceptual linear prediction (PLP) coefficients and cepstral mean and variance normalization.