In this paper we present a novel approach to acoustic model training for non-audible murmur (NAM) recognition using normal speech data transformed into NAM data. NAM is extremely ...
Acoustic imaging is a computationally intensive and ill-conditioned inverse problem, which involves estimating high resolution source distributions with large microphone arrays. I...
HMM-based acoustic models built from bootstrap are generally very large, especially when full covariance matrices are used for Gaussians. Therefore, clustering is needed to compac...
We study key issues related to multilingual acoustic modeling for automatic speech recognition (ASR) through a series of large-scale ASR experiments. Our study explores shared str...
Hui Lin, Li Deng, Dong Yu, Yifan Gong, Alex Acero,...
This paper describes a method for designing oversampled DFT filter banks (FB) optimized for subband acoustic echo cancellation (AEC). For this application, the design requirements...