Multi-parametric source-filter separation of speech and prosodic voice restoration
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In previous work, we presented a case study using an estimated pitch value as the conditioning variable in conditional Gaussians that showed the utility of hiding the pitch values in certain situations or in modeling it independently of the hidden state in ...
As recently introduced, an HMM2 can be considered as a particular case of an HMM mixture in which the HMM emission probabilities (usually estimated through Gaussian mixtures or an artificial neural network) are modeled by state-dependent, feature-based HMM ...
In previous work, we presented a case study using an estimated pitch value as the conditioning variable in conditional Gaussians that showed the utility of hiding the pitch values in certain situations or in modeling it independently of the hidden state in ...
This report investigates the HMM2 approach recently introduced in the framework of automatic speech recognition. HMM2 can be seen as a mixture of HMMs, where a conventional primary HMM (processing a time series of speech data) is supported on a lower level ...
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The purpose of this paper is to investigate the behavior of HMM2 models for the recognition of noisy speech. It has previously been shown that HMM2 is able to model dynamically important structural information inherent in the speech signal, often correspon ...
This paper investigates possibilities to automatically find a low-dimensional, formant-related physical representation of the speech signal, which is suitable for automatic speech recognition (ASR). This aim is motivated by the fact that formants have been ...
This paper investigates possibilities to automatically find a low-dimensional, formant-related physical representation of the speech signal, which is suitable for automatic speech recognition (ASR). This aim is motivated by the fact that formants have been ...
The paper presents a work-in-progress on several emerging concepts in Automatic Speech Recognition (ASR), that are being currently studied at IDIAP. This work can be roughly categorized into three categories: 1) data-guided features, 2) features based on m ...