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State-of-the-art automatic speech recognition (ASR) systems typically rely on pre-processed features. This paper studies the time-frequency duality in ASR feature extraction methods and proposes extending the standard acoustic model with a complex-valued linear projection layer to learn and optimize features that minimize standard cost functions such as cross-entropy. The proposed Complex Linear Projection (CLP) features achieve superior performance compared to pre-processed Log Mel features.