47 research outputs found
Maximum Likelihood Identification Of A Dynamical System Model For Speech Using The EM Algorithm
No full textGenones: optimizing the degree of mixture tying in a large vocabulary hidden Markov model based speech recognizer
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Robust speech recognition for multiple topological scenarios of the GSM mobile phone system
No full textFast speaker adaptation of large vocabulary continuous density HMM speech recognizer using a basis transform approach
No full textSpeech Emotion Recognition Using Non-Linear Teager Energy Based Features in Noisy Environments
No full textPublication in the conference proceedings of EUSIPCO, Bucharest, Romania, 201
Training Data Clustering For Improved Speech Recognition
No full textWe present an approach to cluster the training data for automatic speech recognition (ASR). A relativeentropy based distance metric between training data clusters is defined. This metric is used to hierarchically cluster the training data. The metric can also be used to select the closest training data clusters given a small amount of data from the test speaker. The selected clusters are then used to estimate a set of hidden Markov models (HMMs) for recognizing the speech from the test speaker. We present preliminary experimental results of the clustering algorithm and its application to ASR. 1 Introduction While progress in ASR has been encouraging, it has become increasingly clear that ASR systems must perform well in the presence of mismatches between the training and testing environments. ASR systems trained in one environment often perform poorly in a new environment due to mismatches between the training and testing conditions. Common sources of mismatches include different tran..
Fast Search Algorithms for Connected Phone Recognition Using the Stochastic Segment Model
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