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The HMM-based TTS can produce a highly intelligible and decent quality voice. However, sometimes the synthesized speech exhibits perceptibly annoying glitches due to F0 extraction errors in the training data and voiced/unvoiced swapping errors in F0 generation. In the conventional MSD based F0 modeling [10], the dual but incompatible two probabilistic(More)
Learning a second language is hard, especially when the learner's brain must be retrained to identify sounds not present in his or her native language. It also requires regular practice, but many learners struggle to find the time and motivation. Our solution is to break down the challenge of mastering a foreign sound system into minute-long episodes of(More)
Multiple input multiple output (MIMO) systems that use antenna arrays at both the transmitter and receiver are gaining much more attention and efforts in wireless communication research due to their potential to increase considerably capacity in mobile cellular communications. However, in the real propagation environment of cellular communications, it is(More)
Two categories of Confidence Measure (CM) approaches for Mandarin command word recognition, i.e., Likelihood Ratio Testing (LRT) based CM and Word Posterior Probability (WPP) based CM, are investigated in this paper. Both Equal Error Rate (EER) and Confidence Error Rate (CER) performances of these approaches are evaluated on two databases: A Mandarin(More)
We propose to train Hidden Markov Model (HMM) by allocating Gaussian kernels non-uniformly across states so as to optimize a selected discriminative training criterion. The optimal kernel allocation problem is first formulated based upon a non-discriminative, Maximum Likelihood (ML) criterion and then generalized to incorporate discrimi-native ones. An(More)
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