Niklas Vanhainen

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We propose the application of a recently introduced inference method, the Block Diagonal Infinite Hidden Markov Model (BDiHMM), to the problem of learning the topology of a Hidden Markov Model (HMM) from continuous speech in an unsupervised way. We test the method on the TiDigits continuous digit database and analyse the emerging patterns corresponding to(More)
This paper presents results for large vocabulary continuous speech recognition (LVCSR) in Swedish. We trained acoustic models on the public domain NST Swedish corpus and made them freely available to the community. The training procedure corresponds to the reference recogniser (RefRec) developed for the SpeechDat databases during the COST249 action. We(More)
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