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This paper proposes an alternative multi-level approach to duration prediction for improving prosody generation in statistical parametric speech synthesis using multiple Gaussian process experts. We use two duration models at different levels, specifically, syllable and phone. First, we individually train syllable- and phone-level duration models. Then, the predictive distributions of syllable 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...
Stochastic turn-taking models use a truncated representation of past speech activity to specify how likely a speaker is to talk at the next instant. An unanswered question in such modeling is how far back to extend the conditioning context. We study this question using Switchboard (English, telephone) and Spontal (Swedish, face-to-face) conversations. We also explore whether to trade off precision...
An acoustic-phonetics based word-independent technique which uses syllable context for classifying the lexical syllable stress of spoken English words is presented. Nucleus based clustering is remarkably successful in moving from word-dependent syllable stress classification which is intrinsically not scalable to word-independent classification. This however is not possible without an inherent drop...
Over the last few decades speech recognition has evolved and matured enough to be used in commercial applications. The applications include automatic dictation software, voice dialling, voice controlled navigation and simple data entry. Automatic Speech Recognition (ASR) deals with automatic conversion of acoustic signals of an utterance into text. In this work speech recognition system for Tamil...
This paper presents a statistical approach to synthesizing emphasized speech based on hidden Markov models (HMMs). Context-dependent HMMs are trained using emphasized speech data uttered by intentionally emphasizing an arbitrary accentual phrase in a sentence. To model acoustic characteristics of emphasized speech, new contextual factors describing an emphasized accentual phrase are additionally considered...
While a wide variety of grammatical mistakes may be observed in the speech of non-native speakers, the types and frequencies of these mistakes are not random. Certain parts of speech, for example, have been shown to be especially problematic for Japanese learners of English [1]. Modeling these errors can potentially enhance the performance of computer-assisted language learning systems. This paper...
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