UT-Scope: Towards LVCSR under Lombard effect induced by varying types and levels of noisy background

Adverse environments impact the performance of automatic speech recognition systems in two ways directly by introducing acoustic mis match between the speech signal and acoustic models, and indirectly by affecting the way speakers communicate to maintain intelligible communication over noise (Lombard effect). Currently, an increasing number of studies have analyzed Lombard effect with respect to speech production and perception, yet limited attention has been paid to its impact on speech systems, especially within a larger vocabulary con text. This study presents a large vocabulary speech material captured in the recently acquired portion of UT-Scope database, produced in several types and levels of simulated background noise (highway, crowd, pink). The impact of noisy background variations on speech parameters is studied together with the effects on automatic speech recognition. Front-end cepstral normalization utilizing a modified RASTA filter is proposed and shown to improve recognition performance in a side-by side evaluation with several common and state-of-the-art normalization algorithms.