Improved ETSI advanced front-end for ASR based on robust complex speech analysis
Keita Higa, Keiichi Funaki · 2016
An automatic speech recognition (ASR) is commonly used in these days. Current ASR systems perform well in ideal environment, however it does not perform well in realistic noisy environment. As a robust ASR, ETSI has standardized Advanced Front-End (AFE) that adopts two-stage of iterative Wiener filter (IWF) to realize a speech enhancement as the front-end of ASR. In the ETSI AFE, FFT is used to estimate speech spectrum that designs the Wiener filter. On the other hand, we have already proposed robust complex speech analysis for an analytic signal. It can estimate more robust and more accurate speech spectrum due to the introduced robust criterion and nature of analytic signal. This paper proposes an improved AFE using wide-band robust ELS (Extended Least Square) complex analysis and real-valued analysis instead of FFT. The experimental results using the CENSREC-2 speech database demonstrates that the performance is improved.