Continuous Arabic Speech Segmentation using FFT Spectrogram

Awais, Mian Muhammad; Ahmad, Waquar; Masud, Shahid; Shamail, Shafay

doi:10.1109/innovations.2006.301939

Cited by 13 publications

(7 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [23], the authors introduced continuous Arabic speech segmentation. They aimed to use high segmentation accuracies in Concatenative Speech Synthesis and Continuous Automatic Speech Recognition Systems.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Algorithms for Real Time Blind Audio Source Separation with Natural Language Detection

Alghamdi¹,

Healy²,

Abdelhafez³

2021

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

The Conv-TasNet and Demucs algorithms, can differentiate between two mixed signals, such as music and speech, the mixing operation proceed without any support information. The network of convolutional time-domain audio separations is used in Conv-TasNet algorithm, while there is a new waveform-to-waveform model in Demucs algorithm. The Demucs algorithm utilizes a procedure like the audio generation model and sizable decoder capacity. The algorithms are not pretrained; so, the process of separation is blindly without any function of three Natural Languages (NL) detection. This research study evaluated the quality and execution time of the separation output signals. It focused on studying the effectiveness of NL in Both algorithms based on four sound signal experiments: (music & male), (music &female), (music & conversation), and finally (music & child). In addition, this research studies three NL, which are English, Arabic and Chinese. The results are evaluated based on R square and mir_eval libraries, mean absolute Error (MAE) scores and root mean square error (RMSE). Conv-TasNet has the highest Signal-to-distortion-Ratio (SDR) score is 9.21 of music at (music & female) experiment, and the highest SDR value of child signal is 8.14. The SDR score of music at (music & female) experiment is 7.8 during the Demucs algorithm, whereas child output signal has the highest SDR score 8.15. However, the average execution time of English experiment of Conv-TasNet is seven times faster than Demucs. For accuracy measure, RMSE indicates absolute values, and MAE handles the errors between observations and prediction signals. Both algorithms show high accuracy and excellent results in the separation process.

show abstract

Section: Related Workmentioning

confidence: 99%

Machine Learning Algorithms for Real Time Blind Audio Source Separation with Natural Language Detection

Alghamdi¹,

Healy²,

Abdelhafez³

2021

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

show abstract

“…Moreover, a TTS system based on the corpus has been evaluated which achieved promising results. M. Awais et al [7] has developed an algorithm for the detection of the phonemes boundaries and identify it as pauses, vowels or consonants. An overall accuracy of 95.39% was reached.…”

Section: Related Workmentioning

confidence: 99%

MFC peak based segmentation for continuous Arabic audio signal

Abdo

Kandil

Fawzy

2014

2nd Middle East Conference on Biomedical Engineering

View full text Add to dashboard Cite

This paper presents an algorithm for segmenting a subset of emphatic and non-emphatic sounds automatically from continuously spoken Arabic speech. The important contribution of this paper is to generate rules for automatic segmentation of these sounds which can be extended to the rest of Arabic sounds. In addition, the findings can be used for other speech analysis problems such as data training for speech recognizers, continuous speech segmentation systems, or to build and label Arabic databases. This study has been done in context of recited principles of the Holy Quran which commonly known as recitation rules. The method developed is based on peaks detection from delta function of Mel Frequency Cepstral Coefficients MFCC .The peaks position is used for boundaries locating of the target sounds within the speech signal. Medium vocabulary speech database was used to evaluate the system performance. The test database contained 80 recited words. Each word was recorded from six different speakers constituting total of 480 words. A significant increase in accuracy rate has been achieved compared to the prior work of [1, 2] by enhancing the developed algorithm. Results show that the enhanced algorithm achieved a segmentation accuracy of up to 90%.

show abstract

“…However, the task of speech segmentation is not without its challenges. Unlike written text, spoken utterances lack the inherent acoustic gaps between word utterances [2,3] Moreover, in continuous speech, phonemes tend to merge, and this challenge is particularly pronounced with consonants due to their brief duration [10]. Additionally, variations in speakers and languages introduce further complexities, as different intonations, rhythms, pitch changes, and distinct sounds emerge within language [11,12].…”

Section: Introductionmentioning

confidence: 99%

Improving Automatic Forced Alignment for Phoneme Segmentation in Quranic Recitation

Alqadasi,

Zeki,

Sunar

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Segmentation plays a crucial role in speech processing applications, where high accuracy is essential. The quest for improved accuracy in automatic segmentation, particularly in the context of the Arabic language, has garnered substantial attention. However, the differences between Qur'an recitation and normal Arabic speech, especially with regard to intonation rules affecting the lengthening of long vowels, pose challenges in segmentation especially for Qur'an recitation. This research endeavors to address these challenges by delving into the domain of automatic segmentation for Qur'an recitation recognition. The proposed scheme employs a hidden Markov models (HMMs) forced alignment algorithm. To enhance the precision of segmentation, several refinements have been introduced, with a primary emphasis on the phonetic model of the Qur'an and Tajweed, particularly the intricate rules governing elongation. These enhancements encompass the adaptation of an acoustic model tailored for Qur'anic recitation as preprocessing and culminate in the development of an algorithm aimed at refining forced alignment based on the phonetic nuances of the Qur'an. These enhancements are seamlessly integrated as post-processing components for the classic HMM-based forced alignment. The research utilizes a comprehensive database featuring recordings from 100 renowned Qur'an reciters, encompassing the recitation of 21 Qur'anic verses (Ayat). Additionally, 30 reciters were asked to record the same verses, incorporating various recitation speed patterns. To facilitate the evaluation process, a Random sample of the Qur'anic database was manually segmented, comprised 21 Ayats, totaling 19,800 words, with 89 unique words (14 verses x 3 recitation levels: fast, slow and normal x 6 readers). The outcomes of this study manifest notable advancements in the alignment of long vowels within Qur'an recitation, all while maintaining the precise alignment of vowels and consonants. Objective comparisons between the proposed automatic methods and manual segmentation were conducted to ascertain the superior approach. The findings affirm that the classic forced alignment method produces satisfactory outcomes when employed on verses lacking long vowels. However, its performance diminishes when confronted with verses containing long vowels. Therefore, the test samples were categorized into three groups based on the presence of long vowels, resulting in a Correct Classification Rate (CCR) that ranged from 6% to 57%, contingent on whether the verse includes long vowels or not. The average CCR across all test samples was 23%. In contrast, the proposed algorithm significantly enhances audio segmentation. It achieved CCR values ranging from 16% to 70% within the same database categories, with an average CCR of 45% across all test samples. This marks a notable advancement of 22% in segmented speech accuracy, particularly within a 30 ms tolerance, for verses containing long vowels.

show abstract

Continuous Arabic Speech Segmentation using FFT Spectrogram

Cited by 13 publications

References 12 publications

Machine Learning Algorithms for Real Time Blind Audio Source Separation with Natural Language Detection

Machine Learning Algorithms for Real Time Blind Audio Source Separation with Natural Language Detection

MFC peak based segmentation for continuous Arabic audio signal

Improving Automatic Forced Alignment for Phoneme Segmentation in Quranic Recitation

Contact Info

Product

Resources

About