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We investigate whether language models used in automatic speech recognition (ASR) should be trained on speech transcripts rather than on written texts. By calculating log-likelihood statistic for part-of-speech (POS) n-grams, we show that there are significant differences between written texts and speech transcripts. We also test the performance of language models trained on speech transcripts and...
The paper presents one of the possible approaches to build a triphone model for automatic speech recognition of Polish. Even though classifiers are well developed and described, such task is not a trivial one because of lack of enough training data and importance of calculation time spent for the training of the model. To overcome this problem, some states are typically tied using data-driven criteria...
The paper presents an evaluation of Polish phone segmentation for different types of phones. The categorisation was done based on acoustic properties. The segmentation method is based on discrete wavelet transform and was already published. The results show that several types of transitions, especially from and to vowels cause more errors than others.
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