Kham Nguyen

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The majority of state-of-the-art speech recognition systems make use of system combination. The combination approaches adopted have traditionally been tuned to minimising Word Error Rates (WERs). In recent years there has been growing interest in taking the output from speech recognition systems in one language and translating it into another. This paper(More)
In this paper, we show the progress for Arabic speech recognition by incorporating contextual information into the process of morphological decomposition. The new approach achieves lower out-of-vocabulary and word error rates when compared to our previous work, in which the morphological decomposition relies on word-level information only. We also describe(More)
In this paper, we present a method to extract probabilistic acoustic features by using the Adaptive Boosting algorithm (AdaBoost). We build phoneme Gaussian mixture classifiers, and use AdaBoost to enhance the classification performance. The outputs from AdaBoost are the posterior probabilities for each frame given all phonemes. Those posterior features are(More)
Citation Tim Ng et al. " Improved morphological decomposition for Arabic broadcast news transcription. Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. The MIT Faculty has made this article openly available. Please share how this access benefits(More)
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