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Recently, the speaker code based adaptation has been successfully expanded to recurrent neural networks using bidirectional Long Short-Term Memory (BLSTM-RNN) [1]. Experiments on the small-scale TIMIT task have demonstrated that the speaker code based adaptation is also valid for BLSTM-RNN. In this paper, we evaluate this method on large-scale task and introduce an error normalization method to balance...
Among techniques used in condition monitoring, those for prognostics are the most challenging. This paper presents a Hidden Markov Model (HMM) based approach for prognostics in TCM. A HMM model usually employs a typical working condition for establishing and verifying the model. However, in tool condition monitoring (TCM), the cutting tool encounters a range of cutting conditions. It is not economical...
In order to meet the demand of visual behavior recognition, we introduce Fractal Conditional Random Field (FCRF) model. FCRF model has improved Latent-Dynamic Conditional Random Field (LDCRF), and proposed the concept of fractal labels that define the integrity and directionality of human behavior. FCRF model overcomes real-time issues of the Hidden Conditional Random Field (HCRF) and the problem...
In this study, we propose increasing discriminative power on the maximum a posteriori (MAP)-based mapping function estimation for acoustic model adaptation. Based on the effective and stable learning advantages of MAP-based estimation, we incorporate a discriminative term and derive a new objective function. By applying the new function for online mapping function estimation, we developed discriminative...
We propose an environment population projection (EPP) approach for rapid acoustic model adaptation to reduce environment mismatches with limited amounts of adaptation data. This approach consists of two stages: population construction and projection. In the population construction stage, we apply a sampling scheme on the adaptation data to construct an environment population based on acoustic models...
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