Continuous Hindi speech recognition using Gaussian mixture HMM

Kuamr, Ankit; Dua, Mohit; Choudhary, Tripti

doi:10.1109/sceecs.2014.6804519

Cited by 24 publications

(9 citation statements)

References 4 publications

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“…A pesar del gran número de métodos, procedimientos y técnicas para extraer características relevantes de señales de audio (Fu, et al, 2012;Kuamr, et al, 2014;Yusnita, et al, 2011), el computo de los MFCC ha mostrado ser una de las herramientas más utilizadas para procesas ese tipo de señales, debido a su robustez y calidad en las características que entrega. En la figura 2 se muestran los procesos más importantes para lograr la obtención de MFCC para una señal de voz.…”

Section: Coeficientes Cepstrales De Las Frecuencias De Mel (Mfcc)unclassified

Reconocimiento de Patrones Vocálicos mediante la implementación de una red Neuronal Artificial Utilizando Sistemas Embebidos

2016

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ResumenEn este trabajo se presentan los resultados del diseño y desarrollo de una red neuronal de tipo feed-forward en un sistema embebido, para identificar patrones asociados con la pronunciación de fonemas vocálicos del idioma español. Para el entrenamiento de la red fueron utilizados los coeficientes cepstrales de las frecuencias de Mel (MFCC), extraídos a partir de señales de audio provenientes de la pronunciación de las vocales abiertas (/a/, /e/ y /o/). La captura de nuevas muestras se realizó a través de un computador y un sistema embebido para la evaluación, comprobación y comparación del desempeño de la red neuronal. Se obtuvo un porcentaje de identificación correcta por encima del 98%. Esto indica que el error en la separación de clases fue inferior al 2% en la red neuronal al momento de evaluar patrones asociados a alguna de las tres vocales. Basado en los resultados obtenidos se concluye que la implementación de algoritmos de inteligencia artificial para tareas de clasificación en sistemas embebidos es factible, y presenta resultados similares a los que tendría el mismo sistema operando con los recursos de un computador. Palabras clave: red neuronal; señales de audio; sistema embebido; coeficientes cepstrales; reconocimiento del habla Recognition of Vowel Patterns by Implementing an Artificial Neural Network in an Embedded System AbstractIn this work the results of the design and development of a neural network type feed-forward in an embedded system, to identify patterns associated with the pronunciation of vowels of Spanish language are presented. For the training of the network Mel frequencies cepstrals coefficients extracted from audio signals related to the pronunciation of the open vowels (/ a /, /e/ and /o/) were used. The capture of new samples was performed by using a computer and an embedded system to evaluate, test and compare the performance of the neural network. A percentage of correct identification above 98% was obtained indicating that the error in the class separation was less than 2% for patterns obtained from any of the three vowels. Based on the results it is concluded that implementation of artificial intelligence algorithms for classification tasks in embedded systems is feasible, and presents similar results to those that would have the same system working with the resources of a computer.

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Section: Coeficientes Cepstrales De Las Frecuencias De Mel (Mfcc)unclassified

Reconocimiento de Patrones Vocálicos mediante la implementación de una red Neuronal Artificial Utilizando Sistemas Embebidos

2016

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“…In 2014, A. Kumar [9]investigated various Gaussian mixture HMM using Mel frequency cepstral coefficient (MFCC) as features. They had used 16 KHz sampling rate with hamming window size of 25 milliseconds and frame size as 10 milliseconds.…”

Section: Related Workmentioning

confidence: 99%

“…This method has given promising results with all the speech recognition systems in noise free environments [2]. The use of 39 Mel coefficients is generally common in automatic speech recognition [9]. The main drawback of this method is that it doesn't work in noisy speech as it is dependent on spectral features only.…”

Section: Mel-frequency Cepstral Coefficient (Mfcc)mentioning

confidence: 99%

Spectral Features Analysis for Hindi Speech Recognition System

Garg¹

2016

IJRET

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Automatic speech recognition refers to recognizing the speech utterances and converting them to text through machines. For this purpose, the features forms an extremely important part. The richness of features will predict the performance of the overall system. So, this paper deals with the various speech features that can used for Hindi speech that has been tested for many other languages. In this work, MFCC, PLP, EFCC and LPC have been tested against Hindi Speech Corpus using HMM toolkit HTK 3.4.1. These features have been evaluated using common environment. The main objective of this paper is to summarize and compare the traditional and newer feature extraction methodology in automatic speech recognition system. This work favours EFCC features over other features. EFCC have shown a significant improvement in noisy environment in automatic speech recognition system.

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“…Voice recognition has gained prominence and use with the rise of AI and intelligent assistant. Voice recognition systems enable user to interact with simply way of speaking [6].…”

Section: Introductionmentioning

confidence: 99%

Smart Voice Assistant for Library System

S¹,

Prathinan²,

Suresh³

et al. 2020

Int. Res. J. multidiscip. Technovation

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Library plays an important role in student’s life. It is also helpful for the research scholars. Today libraries are established in all places. Finding a book in the library is a herculean task. Some people even loose interest while searching the required book. Searching for books in computer also takes much time. In our project we use voice recognition to find books which will make the herculean task easier. Even the people without system knowledge can access this and find books in an easier way. It reduces the time in searching books and makes the library user friendly.

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Continuous Hindi speech recognition using Gaussian mixture HMM

Cited by 24 publications

References 4 publications

Reconocimiento de Patrones Vocálicos mediante la implementación de una red Neuronal Artificial Utilizando Sistemas Embebidos

Reconocimiento de Patrones Vocálicos mediante la implementación de una red Neuronal Artificial Utilizando Sistemas Embebidos

Spectral Features Analysis for Hindi Speech Recognition System

Smart Voice Assistant for Library System

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