Title :
Articulatory Information for Noise Robust Speech Recognition
Author :
Mitra, Vikramjit ; Nam, Hosung ; Espy-Wilson, Carol ; Saltzman, Elliot ; Goldstein, Louis
Author_Institution :
Inst. for Syst. Res., Univ. of Maryland, College Park, MD, USA
Abstract :
Prior research has shown that articulatory information, if extracted properly from the speech signal, can improve the performance of automatic speech recognition systems. However, such information is not readily available in the signal. The challenge posed by the estimation of articulatory information from speech acoustics has led to a new line of research known as “acoustic-to-articulatory inversion” or “speech-inversion.” While most of the research in this area has focused on estimating articulatory information more accurately, few have explored ways to apply this information in speech recognition tasks. In this paper, we first estimated articulatory information in the form of vocal tract constriction variables (abbreviated as TVs) from the Aurora-2 speech corpus using a neural network based speech-inversion model. Word recognition tasks were then performed for both noisy and clean speech using articulatory information in conjunction with traditional acoustic features. Our results indicate that incorporating TVs can significantly improve word recognition rates when used in conjunction with traditional acoustic features.
Keywords :
neural nets; speech recognition; Aurora-2 speech corpus; acoustic-to-articulatory inversion; articulatory information; automatic speech recognition systems; neural network based speech-inversion model; noise robust speech recognition; speech acoustics; speech signal; vocal tract constriction variables; Acoustics; Adaptation model; Hidden Markov models; Noise; Noise measurement; Speech; Speech recognition; Articulatory phonology; articulatory speech recognition; artificial neural networks (ANNs); noise-robust speech recognition; speech inversion; task dynamic model; vocal-tract variables;
Journal_Title :
Audio, Speech, and Language Processing, IEEE Transactions on
DOI :
10.1109/TASL.2010.2103058