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Speech Analytics Based on Machine Learning

Abstract

In this chapter, the process of speech data preparation for machine learning is discussed in detail. Examples of speech analytics methods applied to phonemes and allophones are shown. Further, an approach to automatic phoneme recognition involving optimized parametrization and a classifier belonging to machine learning algorithms is discussed. Feature vectors are built on the basis of descriptors coming from the music information retrieval (MIR) domain. Then, phoneme classification beyond the typically used techniques is extended towards exploring Deep Neural Networks (DNNs). This is done by combining Convolutional Neural Networks (CNNs) with audio data converted to the time-frequency space domain (i.e. spectrograms) and then exported as images. In this way a two-dimensional representation of speech feature space is employed. When preparing the phoneme dataset for CNNs, zero padding and interpolation techniques are used. The obtained results show an improvement in classification accuracy in the case of allophones of the phoneme /l/, when CNNs coupled with spectrogram representation are employed. Contrarily, in the case of vowel classification, the results are better for the approach based on pre-selected features and a conventional machine learning algorithm.

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Details

Category:
Monographic publication
Type:
rozdział, artykuł w książce - dziele zbiorowym /podręczniku w języku o zasięgu międzynarodowym
Title of issue:
Machine Learning Paradigms :Advances in Data Analytics strony 129 - 157
Language:
English
Publication year:
2019
Bibliographic description:
Korvel G., Kurowski A., Kostek B., Czyżewski A.: Speech Analytics Based on Machine Learning// Machine Learning Paradigms/ ed. George A. Tsihrintzis, Dionisios N. Sotiropoulos, Lakhmi C. Jain Cham: Springer, 2019, s.129-157
DOI:
Digital Object Identifier (open in new tab) 10.1007/978-3-319-94030-4_6
Sources of funding:
Verified by:
Gdańsk University of Technology

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