Text to Speech Synthesizer for Tigrigna Linguistic using Concatenative Based approach with LSTM model

Araya, Mezgebe; Alehegn, Minyechil

doi:10.17485/ijst/v15i1.1935

Cited by 3 publications

(3 citation statements)

References 12 publications

(20 reference statements)

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“…Each feature identified in the voice samples has been analyzed and incorporated into this database. A total of three female speakers provided the voice samples (20,21) . Recording female speakers' voices is required since "Mary" Text to Speech Synthesis needs a female voice as its output.…”

Section: Resultsmentioning

confidence: 99%

Tamil Speech Synthesizer App for Android: Text Processing Module Enhancement

Arulprakash¹,

Synthiya²,

Vijila³

et al. 2023

IJST

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Objectives: Designing dynamic computer systems that are effective, efficient, simple, and satisfying to use is becoming extremely important in this age of information and communication technology. Text to Speech or Speech Synthesis is one of the many methods being investigated by researchers to improve Human-Computer Interaction. The goal here is to improve the text processing component of the Tamil voice synthesizer by including a text normalizer and loan word identification that is efficient and reliable. Methods: Text normalization is conducted on unconstrained Tamil text to turn non-standard terms into common words to reduce confusing utterances during intermediate word processing. Loan/Native words in Tamil literature are detected to enhance the Tamil voice synthesizer system's pronunciation model. Findings: During normalization, non-standard Tamil words are replaced with standard ones to reduce ambiguous utterances during interim processing. A pronunciation model is built to improve the Tamil speech synthesizer system by identifying loan words in Tamil text. A syllable classifier is presented in this study, based on a decision list approach, which can handle various types of non-stationary sounds. Novelty: We also disclose a 'loan/native word classifier' based on multiple linear regressions that perform well even with small words of three syllables. Such sophisticated text processors are required in today's dominating Digital, Information-Communication Technology, and Human-Computer Interaction age.

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Section: Resultsmentioning

confidence: 99%

Tamil Speech Synthesizer App for Android: Text Processing Module Enhancement

Arulprakash¹,

Synthiya²,

Vijila³

et al. 2023

IJST

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“…In the internal states of the network, these activations are retained and may give long-term temporal context data. As the input sequence history progresses, RNNs can take use of a dynamically changing contextual window (13) , (14) .…”

Section: Long Short Term Memory (Lstm)mentioning

confidence: 99%

Use of Bidirectional Long Short Term Memory in Spoken Word Detection with reference to the Assamese language

Kalita¹,

Borbora²,

Nath³

2022

IJST

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Objectives :The proposed method is based on a unique technique of Deep learning for identifying spoken words with reference to Assamese language. Most of the DNN based algorithms have been successfully implemented in the field of image recognition, computer vision, natural language processing and medical picture analysis. Methods: The method used here is the Bidirectional Long Short Term Memory (BLSTM). BLSTM incorporates both past and future situations together. The speech database for this research work is hired from the repository of Indian Language Technology Proliferation and Development Center (ILTP-DC). This repository contains 32,335 utterances by 1000 numbers of male and female participants, which is comprised of 262 unique Assamese native words. The BLSTM based recognition model is using 10 out of the 262 unique words and the remaining words are used in construction or generation of synthesized sentences. The feature extraction module uses 39 feature coefficients, which are composed of MFCC, ∆MFCC and ∆∆MFCC coefficients. Findings: The Word Error Rate (WER) of the BLSTM based recognition model is 18.84% with an average accuracy of 98.12%, which sets one promising benchmark when compared to recent findings. Novelty: In this work an attempt has been made with a different approach to detect certain keywords of Assamese language by adopting deep learning methodology. The future objective of this proposed work is to improve the detection capability of this model by considering multiple DNN models together in a hybrid approach along with the inclusion of additional features.

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“…The hand-shaped features were attained with the help of a single-layer Convolutional Self-Organizing Map (CSOM) instead of a pre-trained Deep Convolutional Neural Network (CNN). In literature [15], a prototype was developed for a text-to-speech synthesizer for Tigrigna Language.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Driven Arabic Text to Speech Synthesizer for Visually Challenged People

Alnfiai¹,

Almalki²,

Al‐Wesabi³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Text-To-Speech (TTS) is a speech processing tool that is highly helpful for visually-challenged people. The TTS tool is applied to transform the texts into human-like sounds. However, it is highly challenging to accomplish the TTS outcomes for the non-diacritized text of the Arabic language since it has multiple unique features and rules. Some special characters like gemination and diacritic signs that correspondingly indicate consonant doubling and short vowels greatly impact the precise pronunciation of the Arabic language. But, such signs are not frequently used in the texts written in the Arabic language since its speakers and readers can guess them from the context itself. In this background, the current research article introduces an Optimal Deep Learning-driven Arab Text-to-Speech Synthesizer (ODLD-ATSS) model to help the visually-challenged people in the Kingdom of Saudi Arabia. The prime aim of the presented ODLD-ATSS model is to convert the text into speech signals for visually-challenged people. To attain this, the presented ODLD-ATSS model initially designs a Gated Recurrent Unit (GRU)-based prediction model for diacritic and gemination signs. Besides, the Buckwalter code is utilized to capture, store and display the Arabic texts. To improve the TSS performance of the GRU method, the Aquila Optimization Algorithm (AOA) is used, which shows the novelty of the work. To illustrate the enhanced performance of the proposed ODLD-ATSS model, further experimental analyses were conducted. The proposed model achieved a maximum accuracy of 96.35%, and the experimental outcomes infer the improved performance of the proposed ODLD-ATSS model over other DL-based TSS models.

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Text to Speech Synthesizer for Tigrigna Linguistic using Concatenative Based approach with LSTM model

Cited by 3 publications

References 12 publications

Tamil Speech Synthesizer App for Android: Text Processing Module Enhancement

Tamil Speech Synthesizer App for Android: Text Processing Module Enhancement

Use of Bidirectional Long Short Term Memory in Spoken Word Detection with reference to the Assamese language

Deep Learning Driven Arabic Text to Speech Synthesizer for Visually Challenged People

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