A comparison between allophone, syllable, and diphone based TTS systems for Kurdish language

Barkhoda, Wafa; ZahirAzami, Bahram; Bahrampour, Anvar; Shahryari, Om-Kolsoom

doi:10.1109/isspit.2009.5407540

Cited by 10 publications

(3 citation statements)

References 12 publications

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“…Concatenative synthesis generally produces the most natural soundingsynthesized speech. According to [49], choosing a segment is one of the most challenging steps in concatenative synthesis. There are three main types of concatenative synthesis:…”

Section: Concatenative Synthesismentioning

confidence: 99%

Advancements in Arabic Text-to-Speech Systems: A 22-Year Literature Review

Chemnad

Othman

2023

IEEE Access

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Although there are several speech synthesis models available for different languages tailored to specific domain requirements and applications, there is currently no readily available information on the latest trends in Arabic language speech synthesis. This can make it challenging for beginners to research and develop text-to-speech (TTS) systems for Arabic languages. To address this issue, this article provides a comprehensive overview of several scholars' contributions to the field of Arabic TTS, along with an examination of the unique features of the Arabic language and the corresponding challenges in creating TTS systems. Reporting only on papers discussing Arabic TTS, this systematic review evaluated the available literature published between 2000 and 2022. We conducted a systematic review in six databases using preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines to identify studies that addressed Arabic Text-to-Speech systems. Of a total of 3719 articles identified, only 36 (0.96%) articles met our search criteria. Bibliometric analyses of these studies were conducted and reported. The results highlight the main types of speech synthesis techniques used in TTS systems: concatenative, formant, deep neural network (DNN), hybrid models, and multiagent. The corpora used to develop these systems, as well as the diacritization techniques incorporated, evaluation techniques, and the results of the performance of the systems are reported. Subjective evaluation using the mean opinion score is most applied to measure the accuracy of systems. This study also identifies gaps in the literature and makes recommendations for future research directions.

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Section: Concatenative Synthesismentioning

confidence: 99%

Advancements in Arabic Text-to-Speech Systems: A 22-Year Literature Review

Chemnad

Othman

2023

IEEE Access

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“…And between all three systems, the diphone-based TTS system had the best quality. In the same year in [44] , a comparison was made between the three systems of allophone, syllable, and diphone for the Kurdish TTS system through the use of concatenation. The diphone-based TTS system had the best quality and best Diagnostic-Rhyme-Test DRT (97%).…”

Section: Kurdish Tts Workmentioning

confidence: 99%

End-to-End Kurdish Speech Synthesis Based on Transfer Learning

muhamad

Veisi

2022

PJBAS

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A text-to-speech (TTS) system converts the texts into speech in a specific language. Several TTS systems generate natural-like speech signals in numerous languages, such as English. On the other hand, the Kurdish language has just been examined. Existing preliminary research on Kurdish speech synthesis has utilized old methods and has generated low-quality speech. They also lack important aspects of speech, including intonation, emphasis, and rhythm. Some approaches were presented to address these challenges, including the use of concatenative systems. For example, the unit selection or statistical parametric methods. On the other hand, they need a great deal of time, effort, and domain knowledge. An additional factor for Kurdish speech synthesizers' low performance is the absence of publicly available speech corpora, unlike English, which has many freely-available corpora and audiobooks. The motivation of this paper is to create a Central Kurdish speech corpus and generate a human-like speech from the Kurdish text. This paper explains how to utilize Tacotron 2, an end-to-end neural network architecture and HiFi-GAN vocoder, to produce a high-quality, realistic, and human-like Kurdish voice. This work utilizes "text, audio" pairings, which contain 10 hours of recorded audio samples and texts collected from the Internet and textbooks. It shows how to use English character embedding as the pre-trained knowledge with Kurdish characters as input and how to preprocess these audio examples to get a great outcome. Our evaluations for various types of texts show a mean opinion score of 4.1, comparable with state-of-the-art synthesizers in other languages.

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“…-One-to-one mappings (Figure 1(a)), covering a large subset of the characters; -One-to-two mappings (Figure 1(a)), which reflect the inherent discrepancies between the two writing systems [Barkhoda et al 2009]. While transliterating between these two alphabets, the contextual information can provide hints in choosing the right counterpart.…”

Section: Script Diversitymentioning

confidence: 99%

Towards Kurdish Information Retrieval

Esmaili

Salavati

Datta

2014

ACM Transactions on Asian Language Information Processing

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The Kurdish language is an Indo-European language spoken in Kurdistan, a large geographical region in the Middle East. Despite having a large number of speakers, Kurdish is among the less-resourced languages and has not seen much attention from the IR and NLP research communities. This article reports on the outcomes of a project aimed at providing essential resources for processing Kurdish texts.A principal output of this project is Pewan, the first standard Test Collection to evaluate Kurdish Information Retrieval systems. The other language resources that we have built include a lightweight stemmer and a list of stopwords.Our second principal contribution is using these newly-built resources to conduct a thorough experimental study on Kurdish documents. Our experimental results show that normalization, and to a lesser extent, stemming, can greatly improve the performance of Kurdish IR systems.

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A comparison between allophone, syllable, and diphone based TTS systems for Kurdish language

Cited by 10 publications

References 12 publications

Advancements in Arabic Text-to-Speech Systems: A 22-Year Literature Review

Advancements in Arabic Text-to-Speech Systems: A 22-Year Literature Review

End-to-End Kurdish Speech Synthesis Based on Transfer Learning

Towards Kurdish Information Retrieval

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