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- W144212596 abstract "Hidden Markov Models (HMMs) have been widely used for Automatic Speech Recognition (ASR). Iterative algorithms such as Forward - Backward or Baum-Welch are commonly used to locally optimize HMM parameters (i.e., observation and transition probabilities). However, finding more suitable transition probabilities for the HMMs, which may be phoneme-dependent, may be achievable with other techniques. In this paper we study the application of two Genetic Algorithms (GA) to accomplish this task, obtaining statistically significant improvements on un-adapted and adapted Speaker Independent HMMs when tested with different users." @default.
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- W144212596 date "2012-01-01" @default.
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- W144212596 title "GA Approaches to HMM Optimization for Automatic Speech Recognition" @default.
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- W144212596 doi "https://doi.org/10.1007/978-3-642-31149-9_32" @default.
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