Analysis and Compensation of Lombard Speech Across Noise Type and Levels With Application to In-Set/Out-of-Set Speaker Recognition

Speech production in the presence of noise results in the Lombard effect, which is known to have a serious impact on speech system performance. In this study, Lombard speech produced under different types and levels of noise is analyzed in terms of duration, energy histogram, and spectral tilt. Acoustic-phonetic differences are shown to exist between different ldquoflavorsrdquo of Lombard speech based on analysis of trends from a Gaussian mixture model (GMM)-based Lombard speech type classifier. For the first time, the dependence of Lombard speech on noise type and noise level is established for the purposes of speech processing systems. Also, the impact of the different flavors of Lombard effect on speech system performance is shown with respect to an in-set/out-of-set speaker recognition task. System performance is shown to degrade from an equal error rate (EER) of 7.0% under matched neutral training and testing conditions, to an average EER of 26.92% when trained with neutral and tested with Lombard effect speech. Furthermore, improvement in the performance of in-set/out-of-set speaker recognition is demonstrated by adapting neutral speaker models with Lombard speech data of limited duration. Improved average EERs of 4.75% and 12.37% were achieved for matched and mismatched adaptation and testing conditions, respectively. At the highest noise levels, an EER as low as 1.78% was obtained by adapting neutral speaker models with Lombard speech of limited duration. The study therefore illustrates the impact of Lombard effect on speaker recognition, and effective methods to improve system performance for speaker recognition when train/test conditions are mismatched for neutral versus Lombard effect speech.