DWT features performance analysis for automatic speech recognition of Urdu

Ali, Hazrat; Ahmad, Nasir; Zhou, Xianwei; Iqbal, Khalid; Ali, Sahibzada Muhammad

doi:10.1186/2193-1801-3-204

Cited by 22 publications

(12 citation statements)

References 18 publications

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“…Various ways to perform automatic segmentations are Fourier Transform, Short Term Energy, Minimum Phase Group Delay Method, Wavelet Method, Discrete Wavelet Transform (DWT) and Word Chopper Technique [16]. In this proposed system we used DWT method for segmentation since it uses frequency and time concurrently due to that computing the threshold value will be accurate [17]. The DWT is defined as (1) i,j,k are integer values.…”

Section: A Segmentationmentioning

confidence: 99%

Automatic Speech Recognition with Stuttering Speech Removal using Long Short-Term Memory (LSTM)

Girirajan*¹,

Sangeetha²,

Preethi³

et al. 2020

IJRTE

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 Abstract: Stuttering or Stammering is a speech defect within which sounds, syllables, or words are rehashed or delayed, disrupting the traditional flow of speech. Stuttering can make it hard to speak with other individuals, which regularly have an effect on an individual's quality of life. Automatic Speech Recognition (ASR) system is a technology that converts audio speech signal into corresponding text. Presently ASR systems play a major role in controlling or providing inputs to the various applications. Such an ASR system and Machine Translation Application suffers a lot due to stuttering (speech dysfluency). Dysfluencies will affect the phrase consciousness accuracy of an ASR, with the aid of increasing word addition, substitution and dismissal rates. In this work we focused on detecting and removing the prolongation, silent pauses and repetition to generate proper text sequence for the given stuttered speech signal. The stuttered speech recognition consists of two stages namely classification using LSTM and testing in ASR. The major phases of classification system are Re-sampling, Segmentation, Pre-Emphasis, Epoch Extraction and Classification. The current work is carried out in UCLASS Stuttering dataset using MATLAB with 4% to 6% increase in accuracy when compare with ANN and SVM.

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Section: A Segmentationmentioning

confidence: 99%

Automatic Speech Recognition with Stuttering Speech Removal using Long Short-Term Memory (LSTM)

Girirajan*¹,

Sangeetha²,

Preethi³

et al. 2020

IJRTE

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“…We randomly divide our data into train, validation and test sets with a ratio of 2 : 1 : 1 and observe the results for MFCCs as well as for features learned 4 A useful tutorial on SVM is available from Burges [17] 5 The dataset can be requested via email. 6 Previous experimentations with this dataset for speech recognition applications have been reported by [20,21].…”

Section: Experiments Preparationmentioning

confidence: 99%

Speaker recognition with hybrid features from a deep belief network

Ali

Tran

Benetos

et al. 2016

Neural Comput & Applic

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract Learning representation from audio data has shown advantages over the hand-crafted features such as Mel Frequency Cepstral Coefficients (MFCC) in many audio applications. In most of the representation learning approaches, the connectionist systems have been used to learn and extract latent features from the fixed length data. In this paper, we propose an approach to combine the learned features and the MFCC features for speaker recognition task, which can be applied to audio scripts of different length. In particular, we study the use of features from different levels of Deep Belief Network for quantizing the audio data into vectors of audio-word counts. These vectors represent the audio scripts of different length that make them easier to train a classifier. We show in the experiment that the audio-word count vectors generated from mixture of DBN features at different layers give better performance than the MFCC features. We also can achieve further improvement by combining the audio-word count vector and the MFCC features. Permanent repository link

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“…Besides, the speech data used in their experimentation is limited to one particular accent only. Ali et al [13] have reported results on the same dataset of Urdu, as used in the work reported in this paper, and have analyzed the use of Discrete Wavelet Transform (DWT) features and compared the performance with MFCCs. They conclude their discussion in favor of using MFCCs.…”

Section: Related Workmentioning

confidence: 99%

Automatic Speech Recognition of Urdu Digits with Optimal Classification Approach

Ali¹,

An²,

Iqbal³

2015

IJCA

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Speech Recognition for Urdu language is an interesting and less developed task. This is primarily due to the fact that linguistic resources such as rich corpus are not available for Urdu. Yet, few attempts have been made for developing Urdu speech recognition frameworks using the traditional approaches such as Hidden Markov Models and Neural Networks. In this work, we investigate the use of three classification methods for Urdu speech recognition task. We extract the Mel Frequency Cepstral Coefficients, the delta and delta-delta features from the speech data and train the classifiers to perform Urdu speech recognition. We present the performance achieved by training a Support Vector Machine (SVM) classifier, a random forest (RF) classifier and a linear discriminant analysis classifier (LDA) for comparison with SVM. Consequently, the experimental results show that SVM gives better performance than RF and LDA classifiers on this particular task.

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DWT features performance analysis for automatic speech recognition of Urdu

Cited by 22 publications

References 18 publications

Automatic Speech Recognition with Stuttering Speech Removal using Long Short-Term Memory (LSTM)

Automatic Speech Recognition with Stuttering Speech Removal using Long Short-Term Memory (LSTM)

Speaker recognition with hybrid features from a deep belief network

Automatic Speech Recognition of Urdu Digits with Optimal Classification Approach

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