HMM Training with Contaminated Speech Material for Distant-Talking Speech Recognition

A challenging scenario is addressed in which a distant-talking speech recognizer operates in a noisy office environment with model adaptation. The use of a single far microphone as well as that of a microphone array input are investigated. In addition to the benefits from the application of microphone array processing, system robustness is improved by training hidden Markov models with a contaminated version of a clean corpus. This artificial corpus is produced by exploiting information extracted from `real world` acousti scenarios. The resulting models are then used a a starting point for unsupervised incremental adaptation. Experimental results show that improvements in recognition accuracy due to multiple microphones, HMM training on contaminated speech, and incremental adaptation are additive on a connected digits task. Moreover, the results show that unsupervised incremental adaptation receives the benefits of starting from models trained using contaminated speech. A final contribution of the paper refers to the influence of accuracy of speech activity detection, which seems to be relevant when moving towards real applications