Context-dependent duration modeling

The paper proposes a context-dependent duration model and discusses its integration into a first-order hidden Markov model-based speech recognizer. The duration model allows the application of conditional duration probabilities that depend on the durations of neighboring HMM states. This way, it is capable of penalizing or fully preventing unusual durational relations of succeeding states. As the duration model is compiled into a dedicated first-order model topology it can be applied in a single-pass I-best P Viterbi decoder. In addition, this topology facilitates the integration of duration-dependent density functions. In experiments on connected digit recognition we see a relative word error reduction of about 16% with the proposed duration model and another 12% due to duration-dependent densities.