Speech recognition using artificial neural networks

Lim, Chee Peng; Woo, Siew Chan; Loh, Aaron; Osman, Rozianawaty

doi:10.1109/wise.2000.882421

Cited by 29 publications

(6 citation statements)

References 5 publications

(3 reference statements)

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“…A PNN is able to learn the training data in one-pass. In fact, the PNN estimates the probability density function of the data by using the Parzen window [8]. Hence, it is a useful and versatile tool for automatic speech recognition.…”

Section: Probabilistic Neural Networkmentioning

confidence: 99%

Performance Analysis of Thewarped Discrete Cosine Transform Cepstrum with MFCC Using Different Classifiers

Sangwan

Muralishankar

O’Shaughnessy

2005 IEEE Workshop on Machine Learning for Signal Processing

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In this paper, we continue our investigation of the warped discrete cosine transform cepstrum (WDCTC), which was earlier introduced as a new speech processing feature [1]. Here, we study the statistical properties of the WDCTC and compare them with the mel-frequency cepstral coefficients (MFCC). We report some interesting properties of the WD-CTC when compared to the MFCC: its statistical distribution is more Gaussian-like with lower variance, it obtains better vowel cluster separability, it forms tighter vowel clusters and generates better codebooks. Further, we employ the WDCTC and MFCC features in a 5-vowel recognition task using Vector Quantization (VQ), 1-Nearest Neighbour (1-NN), Probabilistic Neural Network (PNN) and Gaussian Discriminant Analysis (GDA) as classifiers. Finally, we discuss the vowel recognition results in the context of the statistical properties of the WDCTC and MFCC. In our experiments, the WDCTC consistently outperforms the MFCC.

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Section: Probabilistic Neural Networkmentioning

confidence: 99%

Performance Analysis of Thewarped Discrete Cosine Transform Cepstrum with MFCC Using Different Classifiers

Sangwan

Muralishankar

O’Shaughnessy

2005 IEEE Workshop on Machine Learning for Signal Processing

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“…A neural network was chosen as a machine learning method for constructing the classifier. The use of such networks is typical for solving a variety of speech analysis problems, such as speech recognition [9,10], authentication [11,12], sentiment determination [13], and others. For this reason, it was decided to use this particular type of city when constructing the classifier.…”

Section: Speech Quality Assessment Based On the Classification Problemmentioning

confidence: 99%

Assessment of Speech Quality during Speech Rehabilitation Based on the Solution of the Classification Problem

Kostyuchenko¹,

Rakhmanenko²,

Shelupanov³

et al. 2021

Preprint

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The article considers an approach to the problem of assessing the quality of speech during speech rehabilitation as a classification problem. For this, a classifier is built on the basis of an LSTM neural network for dividing speech signals into two classes: before the operation and immediately after. At the same time, speech before the operation is the standard to which it is necessary to approach in the process of rehabilitation. The metric of belonging of the evaluated signal to the reference class acts as an assessment of speech. An experimental assessment of rehabilitation sessions and a comparison of the resulting assessments with expert assessments of phrasal intelligibility were carried out.

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“…Savin et al [19] employed an ANN for classification. ANN does not have structured methodology as well as time-consuming for large networks [32].…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Bidirectional LSTM based Detection of Prolongation and Repetition in Stuttered Speech using Weighted MFCC

Gupta¹,

Shukla²,

Shukla³

et al. 2020

IJACSA

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Stuttering is a neuro-development disorder during which normal speech flow is not fluent. Traditionally Speech-Language Pathologists used to assess the extent of stuttering by counting the speech disfluencies manually. Such sorts of stuttering assessments are arbitrary, incoherent, lengthy, and error-prone. The present study focused on objective assessment to speech disfluencies such as prolongation and syllable, word, and phrase repetition. The proposed method is based on the Weighted Mel Frequency Cepstral Coefficient feature extraction algorithm and deep-learning Bidirectional Long-Short term Memory neural network for classification of stuttered events. The work has utilized the UCLASS stuttering dataset for analysis. The speech samples of the database are initially preprocessed, manually segmented, and labeled as a type of disfluency. The labeled speech samples are parameterized to Weighted MFCC feature vectors. Then extracted features are inputted to the Bidirectional-LSTM network for training and testing of the model. The effect of different hyper-parameters on classification results is examined. The test results show that the proposed method reaches the best accuracy of 96.67%, as compared to the LSTM model. The promising recognition accuracy of 97.33%, 98.67%, 97.5%, 97.19%, and 97.67% was achieved for the detection of fluent, prolongation, syllable, word, and phrase repetition, respectively.

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Speech recognition using artificial neural networks

Cited by 29 publications

References 5 publications

Performance Analysis of Thewarped Discrete Cosine Transform Cepstrum with MFCC Using Different Classifiers

Performance Analysis of Thewarped Discrete Cosine Transform Cepstrum with MFCC Using Different Classifiers

Assessment of Speech Quality during Speech Rehabilitation Based on the Solution of the Classification Problem

Deep Learning Bidirectional LSTM based Detection of Prolongation and Repetition in Stuttered Speech using Weighted MFCC

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