Spiral representations in end-to-end Bengali articulatory feature identification
Morshed, Mahir
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https://hdl.handle.net/2142/104030
Description
Title
Spiral representations in end-to-end Bengali articulatory feature identification
Author(s)
Morshed, Mahir
Contributor(s)
Hasegawa-Johnson, Mark
Issue Date
2019-05
Keyword(s)
articulatory feature identification
Bengali speech recognition
connectionist temporal classification
discrete wavelet coefficients
Abstract
The use of end-to-end neural network architectures for speech recognition
applications has brought a transition from using mappings of a speech signal's
frequency spectra as inputs for a model to using the frequency spectra
themselves as inputs. Such architectures, however, may attain different levels
of recognition accuracy for certain tasks when presented with alternate representations
of training data, such as rescaled and transformed spectra. This
thesis presents the findings
of an investigation into using such transformed
representations to develop a model for identifying different
articulatory feature
classes in read Bengali speech using connectionist temporal
classification
on a gated recurrent unit-based network setup. Audio from a variety of
speakers was used to train such a setup to discern places or manners of articulation
of individual speech sounds within a given utterance. The results
of error rate comparisons when given transformed inputs under consistent network configurations
suggest that certain signal representations provide
better performance in identifying different
articulatory feature classes.
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