Tommi Lahti

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State-of-the-art automatic analysis tools for personal audio con-tent management are discussed in this paper. Our main target is to create a system, which has several co-operating management tools for audio database and which improve the results of each other. Bayesian networks based audio classification algorithm provides classification into four main(More)
In this paper, we propose a new approach to reduce the memory footprint of HMM based ASR systems. The proposed method involves three steps. Starting from the continuous density HMMs, mixture variances are tied using k-means based vector quantization. Next, the re-estimation of the resulted models is performed with tied variances. Finally, scalar(More)
In this paper, a novel Out-of-Vocabulary (OOV) word detection method relying on phoneme-level acoustic measures and Support Vector Machines (SVM) is proposed. Word level OOV scores are computed from the phoneme level in-vocabulary (IV) and OOV information provided by an HMM based speech recognizer. The OOV word decision is based on the confidence feature(More)
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