Comparative Analysis of LPCC, MFCC and BFCC for the Recognition of Hindi Words using Artificial Neural Networks

Gulzar, Taabish; Singh, Anand; Sharma, Sandeep

doi:10.5120/17740-8271

Cited by 38 publications

(14 citation statements)

References 12 publications

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“…The aim of pre-emphasis is to equal spectral energy by empowering high-frequency components in a speech signal. LPCC is a spectral feature used in the implementation of practical sound recognition systems [40,41].…”

Section: Linear Prediction Cepstral Coefficientsmentioning

confidence: 99%

Real-Time Detection of Important Sounds with a Wearable Vibration Based Device for Hearing-Impaired People

Yağanoğlu

Köse

2018

Electronics

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Hearing-impaired people do not hear indoor and outdoor environment sounds, which are important for them both at home and outside. By means of a wearable device that we have developed, a hearing-impaired person will be informed of important sounds through vibrations, thereby understanding what kind of sound it is. Our system, which operates in real time, can achieve a success rate of 98% when estimating a door bell ringing sound, 99% success identifying an alarm sound, 99% success identifying a phone ringing, 91% success identifying honking, 93% success identifying brake sounds, 96% success identifying dog sounds, 97% success identifying human voice, and 96% success identifying other sounds using the audio fingerprint method. Audio fingerprint is a brief summary of an audio file, perceptively summarizing a piece of audio content. In this study, our wearable device is tested 100 times a day for 100 days on five deaf persons and 50 persons with normal hearing whose ears were covered by earphones that provided wind sounds. This study aims to improve the quality of life of deaf persons, and provide them a more prosperous life. In the questionnaire performed, deaf people rate the clarity of the system at 90%, usefulness at 97%, and the likelihood of using this device again at 100%.

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Section: Linear Prediction Cepstral Coefficientsmentioning

confidence: 99%

Real-Time Detection of Important Sounds with a Wearable Vibration Based Device for Hearing-Impaired People

Yağanoğlu

Köse

2018

Electronics

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“…MFCC are Cepstral coefficients computed on a warped frequency scale which is described on the basis of human auditory perception and LPCC are Cepstral coefficients that correspond to the human articulatory system based on linear prediction. [5]. 11.…”

Section: VImentioning

confidence: 99%

Comparative study of voice print Based acoustic features: MFCC and LPCC

Jain¹,

Kishore²

2017

IJAEMS

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“…An exception was Chee et al [9], who applied LPCCs with k-nearest-neighbors and linear discriminant analysis classifiers to automatically detect prolongations and repetition stutters, with recognition accuracy up to 89.77%. In the related field of automatic speech recognition ()ASR), MFCCs have consistently generated better results than LPCCs [10,11]; to see if this trend extends to the domain of dysfluency detection, we compare these feature types with DNNs. …”

Section: Introductionmentioning

confidence: 99%

Automatic dysfluency detection in dysarthric speech using deep belief networks

Oue¹,

Marxer²,

Rudzicz³

2015

Proceedings of SLPAT 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies

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Dysarthria is a speech disorder caused by difficulties in controlling muscles, such as the tongue and lips, that are needed to produce speech. These differences in motor skills cause speech to be slurred, mumbled, and spoken relatively slowly, and can also increase the likelihood of dysfluency. This includes nonspeech sounds, and 'stuttering', defined here as a disruption in the fluency of speech manifested by prolongations, stop-gaps, and repetitions. This paper investigates different types of input features used by deep neural networks (DNNs) to automatically detect repetition stuttering and non-speech dysfluencies within dysarthric speech. The experiments test the effects of dimensionality within Mel-frequency cepstral coefficients (MFCCs) and linear predictive cepstral coefficients (LPCCs), and explore the detection capabilities in dyarthric versus non-dysarthric speech. The results obtained using MFCC and LPCC features produced similar recognition accuracies; repetition stuttering in dysarthric speech was identified correctly at approximately 86% and 84% for non-dysarthric speech. Non-speech sounds were recognized with approximately 75% accuracy in dysarthric speakers.

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Comparative Analysis of LPCC, MFCC and BFCC for the Recognition of Hindi Words using Artificial Neural Networks

Cited by 38 publications

References 12 publications

Real-Time Detection of Important Sounds with a Wearable Vibration Based Device for Hearing-Impaired People

Real-Time Detection of Important Sounds with a Wearable Vibration Based Device for Hearing-Impaired People

Comparative study of voice print Based acoustic features: MFCC and LPCC

Automatic dysfluency detection in dysarthric speech using deep belief networks

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