Jira: a Central Kurdish speech recognition system, designing and building speech corpus and pronunciation lexicon

Veisi, Hadi; Hosseini, Hawre; Mohammadamini, Mohammad; Fathy, W.; Mahmudi, Aso

doi:10.1007/s10579-022-09594-4

Cited by 4 publications

(2 citation statements)

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“…Text normalization is a process to unify the TTS input and acts as an essential step in improving the quality of TTS models. The normalization is much more important when it comes to the Kurdish Language since Kurdish writers and publishers utilize a variety of encoding schemes and orthographic standards [47] . To develop a text-to-speech system, we do text normalization, and the details of normalization completed on the text corpus are presented in [48,49] in our preprocessing stage.…”

Section: Pre-processingmentioning

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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Section: Pre-processingmentioning

confidence: 99%

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

muhamad

Veisi

2022

PJBAS

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“…Using CMUSphinx to create ASR, narrated by a single speaker using Audacity software at a sampling rate of 16000 and a 16-bit rate mono single channel. After that, another attempts for Kurdish language arise in [11] For isolated word recognition, some Arabic papers been reviewed. In [12] proposed, an Arabic digit classification system using 450 Arabic spoken digits.…”

Section: Literature Reviewmentioning

confidence: 99%

Kurdish Speech to Text Recognition System Based on Deep Convolutional-recurrent Neural Networks

Hussein

Mahmood

2022

UHD J SCI TECH

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In recent years, deep learning has had enormous success in speech recognition and natural language processing. In other languages, recent progress in speech recognition has been quite promising, but the Kurdish language has not seen comparable development. There are extremely few research papers on Kurdish speech recognition. In this paper, investigated Gated Recurrent Units (GRUs) which is one of the popular RNN models to recognize individual Kurdish words, and propose a very simplified deep-learning architecture to get more efficient and high accuracy model. The proposed model consists of a combination of CNN and GRU layers. The Kurdish Sorani Speech KSS dataset was created for the speech recognition system, as its 18799 sound files for 500 formal Kurdish words. Finally, the model proposed was trained with collected data and yielded over %96 accuracy. The combination of CNN an RNN (GURs) for speech recognition achieved superior performance compared to the other feed-forward deep neural network models and other statistical methods.

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