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SAT + EP: The eigenphone method with speaker-adaptive training.
SAT + MLLR: The standard MLLR method with speaker-adaptive training.
SAT + EP: The eigenphone method with speaker-adaptive training. .
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It is well known that many conventional speaker adaptation methods, such as MLLR and the eigenvoice method, work substantially better in combination with speaker-adaptive training (SAT [11].
Combined with speaker-adaptive training, performance is further improved.
This paper deals with speaker-adaptive speech recognition for large spoken archives.
This training starts with a flat-start initialization of context-independent phonetic Hidden Markov Models (HMMs) and ends with speaker adaptive training (SAT) of state-clustered triphone HMMs with Gaussian mixture model (GMM) output densities.
To evaluate its validity, this paper will combine the new reconstruction method with speaker recognition system.
In the last two decades, large progress has been made in the areas of feature-normalization, speaker-independent and speaker-adaptive acoustic modeling, and robust estimation methods for statistical language models.
Speaker-adaptive training (SAT) is performed with a single fMLLR transform estimated per speaker.
speaker-adaptive training.
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