Dynamic Bayesian network based speech recognition with pitch and energy as auxiliary variables

Modeling auxiliary information in Bayesian network based ASR

Automatic speech recognition bases its models on the acoustic features derived from the speech signal. Some have investigated replacing or supplementing these features with information that can not be precisely measured (articulator positions, pitch, gender, etc.) automatically. Consequently, automatic estimations of the desired information would be generated. This data can degrade performance ...

Mixed Bayesian Networks with Auxiliary Variables for Automatic Speech Recognition

In standard automatic speech recognition (ASR), hidden Markov models (HMMs) calculate their emission probabilities by an artificial neural network (ANN) or a Gaussian distribution conditioned only upon the hidden state variable. Recent work [12] showed the benefit of conditioning the emission distributions also upon a discrete auxiliary variable, which is observed in training and hidden in reco...

Using Auxiliary Sources of Knowledge for Automatic Speech Recognition

Standard hidden Markov model (HMM) based automatic speech recognition (ASR) systems usually use cepstral features as acoustic observation and phonemes as subword units. Speech signal exhibits wide range of variability such as, due to environmental variation, speaker variation. This leads to different kinds of mismatch, such as, mismatch between acoustic features and acoustic models or mismatch ...

Hybrid HMM/BN LVCSR system integrating multiple acoustic features

In current HMM based speech recognition systems, it is difficult to supplement acoustic spectrum features with additional information such as pitch, gender, articulator positions, etc. On the other hand, Dynamic Bayesian Networks (DBN) allow for easy combination of different features and make use of conditional dependencies between them. However, lack of efficient algorithms has prevented their...

Automatic Speech Recognition using Pitch Information in Dynamic Bayesian Networks