A Comparative Study of Feature Extraction Techniques for Speech Recognition System

Kurzekar, Pratik K.; Deshmukh, Ratnadeep R.; Waghmare, Vishal B.; Shrishrimal, Pukhraj P.; Ambedkar, Babasaheb

doi:10.15680/ijirset.2014.0312034

Cited by 58 publications

(23 citation statements)

References 15 publications

(5 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consequently, Hamming windowing was responsible for creating a window shape by considering the next block of the feature extraction processing chain and integrating all the closest frequency lines. Thus, Hamming windows were computed based on equation (1) and equation ( 2) [22].…”

Section: Figure 3: Block Diagram Of Mfccmentioning

confidence: 99%

Comparative Study of different types of RNN in Speech Classification

Ragheb¹,

Gody

Said

2021

The Egyptian Journal of Language Engineering

View full text Add to dashboard Cite

This paper introduces different pre-processing classification models and their performance in the Automatic Speech Recognition system. Other Recurrent Neural Network (RNN) architectures have been tested for this problem, such as RNN cells (RNN), bidirectional RNN (BRNN), Long Short-Term Memory (LSTM), and bidirectional LSTM. Mainly, two features have been considered. First, Mel Frequency Cepstral Coefficient (MFCC) plus delta and delta-delta coefficients (39 parameters) have been used. Second, MFCC quantization using Vector Quantization technique has been used as features. All models have been trained on TIMIT database. Vowels, nasals, fricatives, plosives, and silences have been chosen as syllable classes for classification. Experiment results show that BRNN-MFCC-5-{30,30,20,25,25} system give the highest accuracy. It achieved 92.6%. In similar work of using RNN in classification, 83% accuracy was achieved by [1], and 95% had been achieved by [2]. It is also noticeable that the results obtained by using HMM in a similar problem are 80% by[19] and 81.01% by [17].

show abstract

Section: Figure 3: Block Diagram Of Mfccmentioning

confidence: 99%

Comparative Study of different types of RNN in Speech Classification

Ragheb¹,

Gody

Said

2021

The Egyptian Journal of Language Engineering

View full text Add to dashboard Cite

show abstract

“…These features extracted are unique to others. In the training phase, GMM trains the extracted features [5].Expectation and Maximization algorithm is used to train the extracted features of human voice in the system and the data base for the same is created [2]. In the testing phase after the feature extraction the features are compared with the trained database to predict the accent.…”

Section: The Testing Phasementioning

confidence: 99%

Domestic Language Accent Detector Using MFCC and GMM

K¹,

Joseph²

2020

International Journal of Applied Engineering Research

View full text Add to dashboard Cite

“…MFCC is used to identify airline reservation, numbers spoken into a telephone and voice recognition system for security purpose. Some modifications have been proposed to the basic MFCC algorithm for better robustness, such as by lifting the log-melamplitudes to an appropriate power (around 2 or 3) before applying the DCT and reducing the impact of the low-energy parts [4].…”

Section: Mel Frequency Cepstral Coefficients (Mfcc)mentioning

confidence: 99%

“…LPC is generally used for speech reconstruction. LPC method is generally applied in musical and electrical firms for creating mobile robots, in telephone firms, tonal analysis of violins and other string musical gadgets [4].…”

Section: Linear Prediction Coefficients (Lpc)mentioning

confidence: 99%

“…Both hardware and software that have been developed for such tasks have been applied in various fields such as health care, government sectors and agriculture. Speaker recognition is the capability of a software or hardware to receive speech signal, identify the speaker present in the speech signal and recognize the speaker afterwards [4]. Speaker recognition executes a task similar to what the human brain undertakes.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Some Commonly Used Speech Feature Extraction Algorithms

Alim¹,

Rashid²

2018

From Natural to Artificial Intelligence - Algorithms and Applications

View full text Add to dashboard Cite

Speech is a complex naturally acquired human motor ability. It is characterized in adults with the production of about 14 different sounds per second via the harmonized actions of roughly 100 muscles. Speaker recognition is the capability of a software or hardware to receive speech signal, identify the speaker present in the speech signal and recognize the speaker afterwards. Feature extraction is accomplished by changing the speech waveform to a form of parametric representation at a relatively minimized data rate for subsequent processing and analysis. Therefore, acceptable classification is derived from excellent and quality features. Mel Frequency Cepstral Coefficients (MFCC), Linear Prediction Coefficients (LPC), Linear Prediction Cepstral Coefficients (LPCC), Line Spectral Frequencies (LSF), Discrete Wavelet Transform (DWT) and Perceptual Linear Prediction (PLP) are the speech feature extraction techniques that were discussed in these chapter. These methods have been tested in a wide variety of applications, giving them high level of reliability and acceptability. Researchers have made several modifications to the above discussed techniques to make them less susceptible to noise, more robust and consume less time. In conclusion, none of the methods is superior to the other, the area of application would determine which method to select.

show abstract

A Comparative Study of Feature Extraction Techniques for Speech Recognition System

Cited by 58 publications

References 15 publications

Comparative Study of different types of RNN in Speech Classification

Comparative Study of different types of RNN in Speech Classification

Domestic Language Accent Detector Using MFCC and GMM

Some Commonly Used Speech Feature Extraction Algorithms

Contact Info

Product

Resources

About