The mismatch that frequently occurs between the training and testing conditions of an automatic speech recognizer can be e ciently reduced by adapting the parameters of the recognizer to the testing conditions. The maximum likelihood adaptation algorithms for continuous-density hidden-Markov-model (HMM) based speech recognizers are fast, in the sense that a small amount of data is required for ...

This article presents a cross-lingual study for Hungarian and Finnish about the segmentation of continuous speech on word and phrasal level by examination of supra-segmental parameters. A word level segmentationer has been developed which can indicate the word boundaries with acceptable precision for both languages. The ultimate aim is to increase the robustness of speech recognition on the lan...

The currently dominant speech recognition methodology, Hidden Markov Modeling, treats speech as a stochastic random process with very simple mathematical properties. The simplistic assumptions of the model, and especially that of the independence of the observation vectors have been criticized by many in the literature, and alternative solutions have been proposed. One such alternative is segme...

This paper describes a novel method that models the correlation between acoustic observations in contiguous speech segments. The basic idea behind the method is that acoustic observations are conditioned not only on the phonetic context but also on the preceding acoustic segment observation. The correlation between consecutive acoustic observations is modeled by polynomial mean trajectory segme...

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The pitch contour in speech contains information about different linguistic units at several distinct temporal scales. At the finest level, the microprosodic cues are purely segmental in nature, whereas in the coarser time scales, lexical tones, word accents, and phrase accents appear with both linguistic and paralinguistic functions. Consequently, the pitch movements happen on different tempor...

AbsfructThis paper describes a large vocabulary, speakerindependent, continuous speech recognition system which is based on hidden Markov modeling (HMM) of phoneme-sized acoustic units using continuous mixture Gaussian densities. A bottom-up merging algorithm is developed for estimating the parameters of the mixture Gaussian densities, where the resultant number of mixture components is proport...

Improving phoneme recognition has attracted the attention of many researchers due to its applications in various fields of speech processing. Recent research achievements show that using deep neural network (DNN) in speech recognition systems significantly improves the performance of these systems. There are two phases in DNN-based phoneme recognition systems including training and testing. Mos...