Correcting Confusion Matrices for Phone Recognizers
Graph Chatbot
Chattez avec Graph Search
Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.
AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.
The recognition of speech in meetings poses a number of challenges to current Automatic Speech Recognition (ASR) techniques. Meetings typically take place in rooms with non-ideal acoustic conditions and significant background noise, and may contain large s ...
In this paper, we investigate the possibility of enhancing state-of-the-art HMM-based speech recognition systems using data-driven techniques, where whole set of training utterances is used as reference models and recognition is then performed through the ...
The purpose of this paper is to unify several of the state-of-the-art score normalization techniques applied to text-independent speaker verification systems. We propose a new framework for this purpose. The two well-known Z- and T-normalization techniques ...
The purpose of this paper is to unify several of the state-of-the-art score normalization techniques applied to text-independent speaker verification systems. We propose a new framework for this purpose. The two well-known Z- and T-normalization techniques ...
This paper discusses the evaluation of automatic speech recognition (ASR) systems developed for practical applications, suggesting a set of criteria for application-oriented performance measures. The commonly used word error rate (WER), which poses ASR eva ...
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 ...
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 ...
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 ...
Automatic Speech Recognition systems typically use smoothed spectral features as acoustic observations. In recent studies, it has been shown that complementing these standard features with pitch frequency could improve the system performance of the system. ...
In this paper, we investigate the possibility of enhancing state-of-the-art HMM-based speech recognition systems using data-driven techniques, where whole set of training utterances is used as reference models and recognition is then performed through the ...
