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In this book, hierarchical structures based on neural networks are
investigated for automatic speech recognition. These structures are
mainly evaluated within the phoneme recognition task under the
Hybrid Hidden Markov Model/Artificial Neural Network (HMM/ANN)
paradigm. The baseline hierarchical scheme consists of two levels
each which is based on a Multilayered Perceptron (MLP).
Additionally, the output of the first level is used as an input for
the second level. This system can be substantially speeded up by
removing the redundant information contained at the output of the
first level.
Novel Techniques for Dialectal Arabic Speech describes approaches
to improve automatic speech recognition for dialectal Arabic. Since
speech resources for dialectal Arabic speech recognition are very
sparse, the authors describe how existing Modern Standard Arabic
(MSA) speech data can be applied to dialectal Arabic speech
recognition, while assuming that MSA is always a second language
for all Arabic speakers. In this book, Egyptian Colloquial Arabic
(ECA) has been chosen as a typical Arabic dialect. ECA is the first
ranked Arabic dialect in terms of number of speakers, and a high
quality ECA speech corpus with accurate phonetic transcription has
been collected. MSA acoustic models were trained using news
broadcast speech. In order to cross-lingually use MSA in dialectal
Arabic speech recognition, the authors have normalized the phoneme
sets for MSA and ECA. After this normalization, they have applied
state-of-the-art acoustic model adaptation techniques like Maximum
Likelihood Linear Regression (MLLR) and Maximum A-Posteriori (MAP)
to adapt existing phonemic MSA acoustic models with a small amount
of dialectal ECA speech data. Speech recognition results indicate a
significant increase in recognition accuracy compared to a baseline
model trained with only ECA data.
In this book, hierarchical structures based on neural networks are
investigated for automatic speech recognition. These structures are
mainly evaluated within the phoneme recognition task under the
Hybrid Hidden Markov Model/Artificial Neural Network (HMM/ANN)
paradigm. The baseline hierarchical scheme consists of two levels
each which is based on a Multilayered Perceptron (MLP).
Additionally, the output of the first level is used as an input for
the second level. This system can be substantially speeded up by
removing the redundant information contained at the output of the
first level.
Novel Techniques for Dialectal Arabic Speech describes approaches
to improve automatic speech recognition for dialectal Arabic. Since
speech resources for dialectal Arabic speech recognition are very
sparse, the authors describe how existing Modern Standard Arabic
(MSA) speech data can be applied to dialectal Arabic speech
recognition, while assuming that MSA is always a second language
for all Arabic speakers. In this book, Egyptian Colloquial Arabic
(ECA) has been chosen as a typical Arabic dialect. ECA is the first
ranked Arabic dialect in terms of number of speakers, and a high
quality ECA speech corpus with accurate phonetic transcription has
been collected. MSA acoustic models were trained using news
broadcast speech. In order to cross-lingually use MSA in dialectal
Arabic speech recognition, the authors have normalized the phoneme
sets for MSA and ECA. After this normalization, they have applied
state-of-the-art acoustic model adaptation techniques like Maximum
Likelihood Linear Regression (MLLR) and Maximum A-Posteriori (MAP)
to adapt existing phonemic MSA acoustic models with a small amount
of dialectal ECA speech data. Speech recognition results indicate a
significant increase in recognition accuracy compared to a baseline
model trained with only ECA data.
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