A survey of techniques for incremental learning of HMM parameters
The performance of Hidden Markov Models (HMMs) targeted for complex real-world applications are often degraded because they are designed a priori using limited training data and prior knowledge, and because the classification environment changes during operations. Incremental learning of new data sequences allows to adapt HMM parameters as new data becomes available, without having to retrain from the start on all accumulated training data. This paper presents a survey of techniques found in literature that are suitable for incremental learning of HMM parameters. These techniques are classified according to the objective function, optimization technique and target application, involving block-wise and symbol-wise learning of parameters. Convergence properties of these techniques are presented along with an analysis of time and memory complexity. In addition, the challenges faced when these techniques are applied to incremental learning is assessed for scenarios in which the new training data is limited and abundant. While the convergence rate and resource requirements are critical factors when incremental learning is performed through one pass over abundant stream of data, effective stopping criteria and management of validation sets are important when learning is performed through several iterations over limited data. In both cases managing the learning rate to integrate pre-existing knowledge and new data is crucial for maintaining a high level of performance. Finally, this paper underscores the need for empirical benchmarking studies among techniques presented in literature, and proposes several evaluation criteria based on non-parametric statistical testing to facilitate the selection of techniques given a particular application domain.
incremental learning hidden markov model limited training data on-line learning expectationmaximization recursive estimation