On the selection of the impulse responses for distant-speech recognition based on contaminated speech training

Distant-speech recognition represents a technology of fundamental importance for future development of assistive applications characterized by flexible and unobtrusive interaction in home environments. State-of-the-art speech recognition still exhibits lack of robustness, and an unacceptable performance variability, due to environmental noise, reverberation effects, and speaker position. In the past, multi-condition training and contamination methods were explored to reduce the mismatch between training and test conditions. However, the performance evaluation can be biased by factors as limited number of positions of speaker and microphones, adopted set of impulse responses, vocabulary and grammars defining the recognition task.