Jia-Lin Shen

Learn More
This paper presents an entropy-based algorithm for accurate and robust endpoint detection for speech recognition under noisy environments. Instead of using the conventional energy-based features, the spectral entropy is developed to identify the speech segments accurately. Experimental results show that this algorithm outperforms the energy-based algorithms(More)
In this paper, an improved mismatch function by considering signal correlation between speech and noise is proposed to better estimate the noisy speech HMM's. A linearized model based on Taylor series expansion approach is used to approximate the proposed mismatch function. The parameters of the noisy speech HMM's can be estimated more precisely by(More)
In order to pursue high performance of Chinese information access on the Internet, this paper presents an attractive approach with a successful integration of efficient speech recognition and information retrieval techniques. A working system based on the proposed approach for speech retrieval of real-time Chinese netnews services has been implemented and(More)
This paper presents the use of a variety of lters in the temporal trajectories of frequency band spectrum to extract speech recognition features for environmental robustness. Three kind of lters for emphasizing the statistically important parts of speech are proposed. First, a bank of RASTA-like band-pass lters to t the statistical peaks of modulation(More)
The parallel model combination (PMC) technique has been shown to achieve very good performance for speech recognition under noisy conditions. In this approach, the speech signal and the noise are assumed uncorrelated during modeling. In this paper, a new correlated PMC is proposed by properly estimating and modeling the nonzero correlation between the(More)