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State-of-the-art automatic speech recognition (ASR) techniques are typically based on hidden Markov models (HMMs) for the modeling of temporal sequences of feature vectors extracted from the speech signal. At the level of each HMM state, Gaussian mixture m ...
In current automatic speech recognition (ASR) systems, the energy is not used as part of the feature vector in spite of being a fundamental feature in the speech signal. The noise inherent in its estimation degrades the system performance. In this report w ...
In this paper, we present an HMM2 based method for speaker normalization. Introduced as an extension of Hidden Markov Model (HMM), HMM2 differentiates itself from the regular HMM in terms of the emission density modeling, which is done by a set of state-de ...
In this paper, we present a new approach towards high performance speech/music discrimination on realistic tasks related to the automatic transcription of broadcast news. In the approach presented here, the (local) Probability Density Function (PDF) estima ...
One of the difficulties in Automatic Speech Recognizer (ASR) is the pronunciation variability. Each word (modeled by a baseline phonetic transcription in the ASR dictionary) can be pronounced in many different ways depending on many complex qualitative and ...
In this paper, we present a new approach towards user-custom-ized password speaker verification combining the advantages of hybrid HMM/ANN systems, using Artificial Neural Networks (ANN) to estimate emission probabilities of Hidden Markov Models, and Gaus ...
The performance of face verification systems has steadily improved over the last few years, mainly focusing on models rather than on feature processing. State-of-the-art methods often use the gray-scale face image as input. In this paper, we propose to use ...
This paper presents the theoretical basis and preliminary experimental results of a new HMM model, referred to as HMM2, which can be considered as a mixture of HMMs. In this new model, the emission probabilities of the temporal (primary) HMM are estimated ...
HMM2 is a particular hidden Markov model where state emission probabilities of the temporal (primary) HMM are modeled through (secondary) state-dependent frequency-based HMMs [12]. As shown in [13], a secondary HMM can also be used to extract robust ASR fe ...
In this paper, we present a new approach towards user-custom-ized password speaker verification combining the advantages of hybrid HMM/ANN systems, using Artificial Neural Networks (ANN) to estimate emission probabilities of Hidden Markov Models, and Gaus ...
