Annika Hämäläinen

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Standard automatic speech recognition (ASR) systems use acoustic models typically trained with speech of young adult speakers. Ageing is known to alter speech production in ways that require ASR systems to be adapted, in particular at the level of acoustic modeling. This paper reports ASR experiments that illustrate the impact of speaker age on speech(More)
Recent research on the TIMIT database suggests that longer-length acoustic units are better suited for modelling pronunciation variation and long-term temporal dependencies in speech than traditional phoneme-length units, yielding substantial improvements in recognition accuracy [9]. In this paper, we investigate whether similar improvements can be gained(More)
Recent research suggests that modeling coarticulation in speech is more appropriate at the syllable level. However, due to a number of additional factors that affect the way syllables are articulated, creating multiple paths through syllable models might be necessary. Our previous research on longer-length multi-path models in connected digit recognition(More)
Recent research suggests that it is more appropriate to model pronunciation variation with syllable-length acoustic models than with triphones. Due to the large number of factors contributing to pronunciation variation at the syllable level, the creation of multi-path model topologies appears necessary. In this paper, we construct multi-path models using(More)
This paper presents a study of European Portuguese elderly speech, in which the acoustic characteristics of two groups of elderly speakers (aged 60-75 and over 75) are compared with those of young adult speakers (aged 19-30). The correlation between age and a set of 14 acoustic features was investigated, and decision trees were used to establish the(More)
In this paper, we construct context-independent single-path and multi-path syllable models aimed at improved pronunciation variation modelling. We use phonetic transcriptions to define the topologies of the syllable models and to initialise the model parameters, and the Baum-Welch algorithm for the re-estimation of the model parameters. We hypothesise that(More)
Recent research suggests that it is more appropriate to model pronunciation variation with syllable-length acoustic models than with context-dependent phones. Due to the large number of factors contributing to pronunciation variation at the syllable level, the creation of multi-path model topologies appears necessary. In this paper, we propose a novel(More)
In this paper, we construct multi-path syllable models using phonetic knowledge for initialising the parallel paths, and a data-driven solution for their re-estimation. We hypothesise that the richer topology of multi-path syllable models would be better at accounting for pronunciation variation than context-dependent phone models that can only account for(More)