Evaluation of missing data techniques for in-car automatic speech recognition
نویسندگان
چکیده
One of the major concerns in deploying speech recognition applications is the lack of robustness of the technology. One key aspect is the sensitivity to stationary or non-stationary background noise. Many approaches to noise robust speech recognition have been proposed before. Some modify the front-end signal processing of the recogniser while others work on the back-end, i.e. modelling and decoding. Stationary noise can be handled with techniques such as spectral subtraction while non-stationary noise is difficult to remove. Approaches that can handle non-stationary noise such as parallel model combination [1], blind source separation [2] and model-based decomposition [3], require explicitly modelling the statistics of the noise.
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