488 research outputs found
A Transfer Learning End-to-End ArabicText-To-Speech (TTS) Deep Architecture
Speech synthesis is the artificial production of human speech. A typical
text-to-speech system converts a language text into a waveform. There exist
many English TTS systems that produce mature, natural, and human-like speech
synthesizers. In contrast, other languages, including Arabic, have not been
considered until recently. Existing Arabic speech synthesis solutions are slow,
of low quality, and the naturalness of synthesized speech is inferior to the
English synthesizers. They also lack essential speech key factors such as
intonation, stress, and rhythm. Different works were proposed to solve those
issues, including the use of concatenative methods such as unit selection or
parametric methods. However, they required a lot of laborious work and domain
expertise. Another reason for such poor performance of Arabic speech
synthesizers is the lack of speech corpora, unlike English that has many
publicly available corpora and audiobooks. This work describes how to generate
high quality, natural, and human-like Arabic speech using an end-to-end neural
deep network architecture. This work uses just text, audio
pairs with a relatively small amount of recorded audio samples with a total of
2.41 hours. It illustrates how to use English character embedding despite using
diacritic Arabic characters as input and how to preprocess these audio samples
to achieve the best results
Diacritic Recognition Performance in Arabic ASR
We present an analysis of diacritic recognition performance in Arabic
Automatic Speech Recognition (ASR) systems. As most existing Arabic speech
corpora do not contain all diacritical marks, which represent short vowels and
other phonetic information in Arabic script, current state-of-the-art ASR
models do not produce full diacritization in their output. Automatic text-based
diacritization has previously been employed both as a pre-processing step to
train diacritized ASR, or as a post-processing step to diacritize the resulting
ASR hypotheses. It is generally believed that input diacritization degrades ASR
performance, but no systematic evaluation of ASR diacritization performance,
independent of ASR performance, has been conducted to date. In this paper, we
attempt to experimentally clarify whether input diacritiztation indeed degrades
ASR quality, and to compare the diacritic recognition performance against
text-based diacritization as a post-processing step. We start with pre-trained
Arabic ASR models and fine-tune them on transcribed speech data with different
diacritization conditions: manual, automatic, and no diacritization. We isolate
diacritic recognition performance from the overall ASR performance using
coverage and precision metrics. We find that ASR diacritization significantly
outperforms text-based diacritization in post-processing, particularly when the
ASR model is fine-tuned with manually diacritized transcripts
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Using automatic speech recognition to evaluate Arabic to English transliteration
Increased travel and international communication has led to an increased need for transliteration of Arabic proper names for people, places, technical terms and organisations. There are a variety of available Arabic to English transliteration systems such as Unicode, the Buckwalter Arabic transliteration, and ArabTeX. The transliteration tables have been developed and used by researchers for many years, but there are only limited attempts to evaluate and compare different transliteration systems. This thesis investigates whether or not speech recognition technology could be used to evaluate different Arabic-English transliteration systems. In order to do so there were 5 main objectives: firstly, to investigate the possibility of using English speech recognition engines to recognize Arabic words; secondly, to establish the possibility of automatic transliteration of diacritised Arabic words for the purpose of creating a vocabulary for the speech recognition engine; thirdly, to explore the possibility of automatically generating transliterations of non diacritised Arabic words; fourthly to construct a general method to compare and evaluate different transliteration; and finally, to test the system and use it to experiment with new transliterations ideas
Effectiveness of query expansion in searching the Holy Quran
Modern Arabic text is written without diacritical marks (short vowels), which causes considerable ambiguity at the word level in the absence of context. Exceptional from this is the Holy Quran, which is endorsed with short vowels and other marks to preserve the pronunciation and hence, the correctness of sensing its words. Searching for a word in vowelized text requires typing and matching all its diacritical marks, which is cumbersome and preventing learners from searching and understanding the text. The other way around, is to ignore these marks and fall in the problem of ambiguity. In this paper, we provide a novel diacritic-less searching approach to retrieve from the Quran relevant verses that match a user’s query through automatic query expansion techniques. The proposed approach utilizes a relational database search engine that is scalable, portable across RDBMS platforms, and provides fast and sophisticated retrieval. The results are presented and the applied approach reveals future directions for search engines
Automatically generated, phonemic Arabic-IPA pronunciation tiers for the boundary annotated Qur'an dataset for machine learning (version 2.0)
In this paper, we augment the Boundary Annotated Qur?an dataset published at LREC 2012 (Brierley et al 2012; Sawalha et al 2012a) with automatically generated phonemic transcriptions of Arabic words. We have developed and evaluated a comprehensive grapheme-phoneme mapping from Standard Arabic \ensuremath> IPA (Brierley et al under review), and implemented the mapping in Arabic transcription technology which achieves 100% accuracy as measured against two gold standards: one for Qur?anic or Classical Arabic, and one for Modern Standard Arabic (Sawalha et al [1]). Our mapping algorithm has also been used to generate a pronunciation guide for a subset of Qur?anic words with heightened prosody (Brierley et al 2014). This is funded research under the EPSRC " Working Together" theme
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