Using different acoustic, lexical and language modeling units for ASR of an under-resourced language – Amharic

Tachbelie, Martha Yifiru; Abate, Solomon Teferra; Besacier, Laurent

doi:10.1016/j.specom.2013.01.008

Cited by 34 publications

(28 citation statements)

References 21 publications

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“…Tachbelie et al [11] have examined syllable and hybrid acoustic modeling units based speech recognizers for Amharic. Tachbelie et al [12] have also analyzed the various acoustic, language, and lexical modeling units to develop Amharic ASR system. However, HLTs in general and ASR system in particular have not been investigated for the Chaha language.…”

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confidence: 99%

Investigation of Automatic Speech Recognition Systems via the Multilingual Deep Neural Network Modeling Methods for a Very Low-Resource Language, Chaha

Fantaye¹,

Yu²,

Hailu³

2020

JSIP

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Automatic speech recognition (ASR) is vital for very low-resource languages for mitigating the extinction trouble. Chaha is one of the low-resource languages, which suffers from the problem of resource insufficiency and some of its phonological, morphological, and orthographic features challenge the development and initiatives in the area of ASR. By considering these challenges, this study is the first endeavor, which analyzed the characteristics of the language, prepared speech corpus, and developed different ASR systems. A small 3-hour read speech corpus was prepared and transcribed. Different basic and rounded phone unit-based speech recognizers were explored using multilingual deep neural network (DNN) modeling methods. The experimental results demonstrated that all the basic phone and rounded phone unit-based multilingual models outperformed the corresponding unilingual models with the relative performance improvements of 5.47% to 19.87% and 5.74% to 16.77%, respectively. The rounded phone unit-based multilingual models outperformed the equivalent basic phone unit-based models with relative performance improvements of 0.95% to 4.98%. Overall, we discovered that multilingual DNN modeling methods are profoundly effective to develop Chaha speech recognizers. Both the basic and rounded phone acoustic units are convenient to build Chaha ASR system. However, the rounded phone unit-based models are superior in performance and faster in recognition speed over the corresponding basic phone unit-based models. Hence, the rounded phone units are the most suitable acoustic units to develop Chaha ASR systems.

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confidence: 99%

Investigation of Automatic Speech Recognition Systems via the Multilingual Deep Neural Network Modeling Methods for a Very Low-Resource Language, Chaha

Fantaye¹,

Yu²,

Hailu³

2020

JSIP

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“…Some examples are shown in Table 2. The surface realizations of the morphological structure are constrained and modified by a number of language phenomenon such as insertion, deletion, phonetic harmony and weakening (or disharmony, assimilation [4][5][6]). The morphemes have their standard forms and surface forms.…”

Section: Table 1 Example Of Morphological Units Of Uyghur Languagementioning

confidence: 99%

Stem-Affix based Uyghur Morphological Analyzer

Ablimit

Kawahara

Pattar

et al. 2016

IJFGCN

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“…According to the statistics of the frequency of various categories of words in the Uyghur million-word corpus, the most commonly used category is noun. Therefore, by implementing the Uyghur Morphological Re-inflection model of noun class, this article introduces the pre-work before the realization of the model [5].…”

Section: Introductionmentioning

confidence: 99%

Construction of the Uyghur Noun Morphological Re-Inflection Model Based on Hybrid Strategy

Munire

Yang

2019

Applied Sciences

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In this paper, a hybrid strategy of rules and statistics is employed to implement the Uyghur Noun Re-inflection model. More specifically, completed Uyghur sentences are taken as an input, and these Uyghur sentences are marked with part of speech tagging, and the nouns in the sentences remain the form of the stem. In this model, relevant linguistic rules and statistical algorithms are used to find the most probable noun suffixes and output the Uyghur sentences after the nouns are re-inflected. With rules of linguistics artificially summed up, the training corpora are formed by the human–machine exchange. The final experimental result shows that the Uyghur morphological re-inflection model is of high performance and can be applied to various fields of natural language processing, such as Uyghur machine translation and natural language generation.

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Using different acoustic, lexical and language modeling units for ASR of an under-resourced language – Amharic

Cited by 34 publications

References 21 publications

Investigation of Automatic Speech Recognition Systems via the Multilingual Deep Neural Network Modeling Methods for a Very Low-Resource Language, Chaha

Investigation of Automatic Speech Recognition Systems via the Multilingual Deep Neural Network Modeling Methods for a Very Low-Resource Language, Chaha

Stem-Affix based Uyghur Morphological Analyzer

Construction of the Uyghur Noun Morphological Re-Inflection Model Based on Hybrid Strategy

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