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This paper presents a way to perform speaker adaptation for automatic speech recognition using the stream weights in a multi-stream setup, which included acoustic models for "Articulatory Features" such as Rounded or Voiced. We present supervised speaker adaptation experiments on a spontaneous speech task and compare the above stream-based approach to conventional approaches, in which the models, and not stream combination weights, are being adapted. In the approach we present, stream weights model the importance of features such as Voiced for word discrimination, which offers a descriptive interpretation of the adaptation parameters.

On using articulatory features for discriminative speaker adaptation