Training Asynchronous Input/Output Hidden Markov Models

In learning tasks in which input sequences are mapped to output sequences, it is often the case that the input and output sequences are not synchronous. For example, in speech recognition , acoustic sequences are longer than phoneme sequences. Input/Output Hidden Markov Models have already been proposed to represent the distribution of an output sequence given an input sequence of the same length. We extend here this model to the case of asynchronous sequences, and show an Expectation-Maximization algorithm for training such models .