Robust feature extraction methods for speech recognition in noisy environments

Mukhedkar, Ajinkya Sunil; Alex, John Sahaya Rani

doi:10.1109/cnsc.2014.6906692

Cited by 6 publications

(3 citation statements)

References 3 publications

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“…After the windowing process, the FFT is applied and the absolute values it returns are placed in a Mel-filter bank; the log of the filter bank values is calculated and the final MFCC feature vectors are created by applying the discrete cosine transform to each Mel-filter bank. Because it relies on auto-correlaton analysis, MFCC shares the trait with LPC that it is not noise robust [13, p. 358]; although there are many MFCC variants, each with their own improvements and compromises [14]. Other feature extraction techniques include Perceptual Linear Predictive Coefficients (PLP), which are often used in conjunction with RASTA for improved performance; Wavelet-based features; and Linear Predictive Cepstral Coefficients (LPCC), an addition to LPC [13][14][15][16].…”

Section: Feature Extraction Techniques Used In Asr Systemsmentioning

confidence: 99%

“…Because it relies on auto-correlaton analysis, MFCC shares the trait with LPC that it is not noise robust [13, p. 358]; although there are many MFCC variants, each with their own improvements and compromises [14]. Other feature extraction techniques include Perceptual Linear Predictive Coefficients (PLP), which are often used in conjunction with RASTA for improved performance; Wavelet-based features; and Linear Predictive Cepstral Coefficients (LPCC), an addition to LPC [13][14][15][16]. The work performed in [3, p.83] contrasts LPCC and MFCC, demonstrating that LPCC generally results in lower accuracy but has a faster computation rate while MFCC is slower to compute, but often results in improved recognition accuracy.…”

Section: Feature Extraction Techniques Used In Asr Systemsmentioning

confidence: 99%

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A Model for the Application of Automatic Speech Recognition for Generating Lesson Summaries

Blunt¹,

Haskins²

2021

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

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Automatic Speech Recognition (ASR) technology has the potential to improve the learning experience of students in the classroom. This article addresses some of the key theoretical areas identified in the pursuit of implementing a speech recognition system, capable of lesson summary generation in the educational setting. The article discusses: some of the applications of ASR technology in education; prominent feature extraction and speech enhancement techniques typically applied to digital speech; and established neural network-based machine learning models capable of keyword spotting or continuous speech recognition. Following the theoretical investigation, a model is proposed for the implementation of an automatic speech recognition system in a noisy educational environment to facilitate automated, speech-driven lesson summary generation. A prototype system was developed and improved based on this model, ultimately proving itself capable of generating a lesson summary intended to bolster students' secondary contact with lesson content. This topic-oriented lesson summary provides students with a lesson transcript, but also helps them to monitor educator-defined keyword terms, their prevalence and order as communicated in the lesson, and their associations with educatordefined sections of course content. The prototype was developed using the Python programming language with a modular approach so that its implemented Continuous Speech Recognition system and noise management technique could be chosen at run-time. The prototype contrasts the performance of CMUSphinx and Google Speech Recognition for ASR, both accessed via a cloud-based programming library, and compared the change in accuracy when applying noise injection, noise cancellation or noise reduction to the educator's speech. Proof of concept was established using the Google Speech Recognition System, which prevailed over CMUSphinx and enabled the prototype to achieve 100,00% accuracy in keyword identification and association on noise-free speech, contrasted with a 96,93% accuracy in keyword identification and association on noise-polluted speech using a noise-cancellation technique.

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Section: Feature Extraction Techniques Used In Asr Systemsmentioning

confidence: 99%

Section: Feature Extraction Techniques Used In Asr Systemsmentioning

confidence: 99%

A Model for the Application of Automatic Speech Recognition for Generating Lesson Summaries

Blunt¹,

Haskins²

2021

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

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“…These features are further taken as input to the classification algorithms. In this research, Mel-Frequency Cepstral coefficients (MFCC) are computed from the audio commentary because of its wide usage in speech recognition [8]. In literature, various neural networks have been adopted for classification.…”

Section: Proposed Workmentioning

confidence: 99%

Audio Event Identification and Classification for Cricket Sports using LSTM

N.K*,

Alex*

2019

IJRTE

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Abstract: Audio event identification is an emerging research topic to augment the automation of audio tagging, context-based audio event retrieval, audio surveillance and much more. In this research work, audio event classification for cricket commentary is done by using long short term memory (LSTM) neural network. Mel-frequency cepstral coefficients (MFCC) features are extracted from the audio commentary and trained with LSTM neural network. The trained LSTM network is validated and attained an accuracy of 95%.

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Evaluation of Cepstral Features of Speech for Person Identification System Under Noisy Environment

Chaudhari

Alex

2018

Lecture Notes in Electrical Engineering

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Robust feature extraction methods for speech recognition in noisy environments

Cited by 6 publications

References 3 publications

A Model for the Application of Automatic Speech Recognition for Generating Lesson Summaries

A Model for the Application of Automatic Speech Recognition for Generating Lesson Summaries

Audio Event Identification and Classification for Cricket Sports using LSTM

Evaluation of Cepstral Features of Speech for Person Identification System Under Noisy Environment

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