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Speech recognition performance using deep neural network based acoustic models is known to degrade when the acoustic environment and the speaker population in the target utterances are significantly different from the conditions represented in the training data. To address these mismatched scenarios, multi-style training (MTR) has been used to perturb utterances in an existing uncorrupted and potentially...
In this work, a classifier that jointly optimises the expected total classification cost and the energy consumption is presented. A numerical study is provided, where different alternatives are implemented on a hearing aid. Our proposal is capable of automatically classifying the acoustic environment that surrounds the user and choosing the parameters of the amplification that are best adapted to...
The optimized speaker model is trained by many time iterative algorithm based on expectation maximization (Abbr. EM). In the process, the choice of speaker model initial value has great influence on the final recognition effect. The most common algorithms which are used to choose the initial value are K-means algorithm and LBG algorithm at present, but the two algorithms belong to a sort of local...
We propose a recognition method based on statistics through analysis the grammatical and semantic characteristics of the Chinese organization name. This recognition method includes three elements: frequency, part of speech, word length. We use the data in mature collection as training data; separately calculate a candidate organization name's word frequency, part of speech and word length of the contribution...
Neurobiological research has uncovered the existence of cortical neurons in various animal species tuned to particular spectro-temporal modulations (STM) in the auditory stimulus. Other findings indicate that temporal statistics of the resulting neural spike trains may encode the underlying content of species-specific communication calls. With this motivation, we present an alternative approach to...
This paper introduces a series of results and experiments used in the development of a Romanian text-to-speech system, focusing on text statistics. We investigate the presence of several linguistic units used in text-to-speech systems, from phonemes to words. The text corpus we used, News-Romanian (News-RO) comprises 4500 newspaper articles. A subset of it, around 2500 sentences represents the Romanian...
The recognition of prosodic structure is an important research aspect in the field of Text-to-Speech. It is essential to improving the naturalness of machine-synthesized speech. This paper proposes an approach to predicting and assigning prosodic structure automatically for Chinese sentences based on their tree structures. It presents the modeling of a statistical language model based on the simply...
In HMM-based speech synthesis, we usually use complex, context dependent models to characterize prosodically and linguistically rich speech units. It is therefore difficult to prepare training data which can cover all combinatorial possibilities of contexts. A common approach to cope with this insufficient training data problem is to build a clustered tree via the MDL criterion. However, an MDL-based...
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