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- W2576706790 abstract "Several speech processing and audio data-mining applications rely on a description of the acoustic environment as a feature vector for classification. The discriminative properties of the feature domain play a crucial role in the effectiveness of these methods. In this work, we consider three environment identification tasks and the task of acoustic model selection for speech recognition. A set of acoustic parameters and Machine Learning algorithms for feature selection are used and an analysis is performed on the resulting feature domains for each task. In our experiments, a classification accuracy of 100% is achieved for the majority of tasks and the Word Error Rate is reduced by 20.73 percentage points for Automatic Speech Recognition when using the resulting domains. Experimental results indicate a significant dissimilarity in the parameter choices for the composition of the domains, which highlights the importance of the feature selection process for individual applications." @default.
- W2576706790 created "2017-01-26" @default.
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- W2576706790 date "2017-03-01" @default.
- W2576706790 modified "2023-10-14" @default.
- W2576706790 title "Discriminative feature domains for reverberant acoustic environments" @default.
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- W2576706790 doi "https://doi.org/10.1109/icassp.2017.7952257" @default.
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