Acknowledgements
Table 4.1: WER(%) obtained in a distant-talking scenario with real and simulated data across different acoustic models and microphone processing.
['[EMPTY]', '[ITALIC] Single Distant Microphone Real Data', '[ITALIC] Single Distant Microphone Sim Data', '[ITALIC] D&S Beamforming Real Data', '[ITALIC] D&S Beamforming Sim Data']
The “mono” model is a context-independent GMM model (monophones), “tri4” is a context-dependent GMM model based on Speaker Adaptive Training (SAT), while “DNN” represents the considered FF-DNN model. One can also note that, as hoped, a similar performance trend between the datasets is reached when applying beamforming. For instance, in the case of real data coupled with FF-DNN acoustic models, delay-and-sum beamforming leads to a relative improvement of about 12% over the single microphone case, which is similar to the improvement of 13% obtained with the simulated data.