Development of Application Specific Continuous Speech Recognition System in Hindi

Gaurav, Gaurav; Deiv, D. Shakina; Sharma, Gopal Krishna; Bhattacharya, Mahua

doi:10.4236/jsip.2012.33052

Cited by 16 publications

(6 citation statements)

References 10 publications

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“…Several models may be used for acoustic modeling. The Hidden Markov Model (HMM) is a popular technique since it is effective for both training and recognition [9].…”

Section: ) Acoustic Modelingmentioning

confidence: 99%

Pronunciation Scoring With Goodness of Pronunciation and Dynamic Time Warping

Sheoran¹,

Bajgoti²,

Gupta³

et al. 2023

IEEE Access

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The current pronunciation scoring based on Goodness of Pronunciation (GOP) uses posterior probabilities of the Acoustic Models. Such algorithms suffer from generalization since they are utilized to determine a score metric for each phoneme rather than on the completeness or comparison with the ideal utterance of the words. In this paper, a novel method is proposed for computing scores calculated using combined scores of prosodic, fluency, completeness, and accuracy. This is achieved using contextaware GOP in conjugation with dynamic time warping (DTW) matching of the pitch contours of a weighted average of the context tokens found in the audio file that is rich in mispronounced phonemes. The proposed work gives flexibility in tuning the results according to different speech aspects based on a single hyperparameter. The results achieved are encouraging and have been validated on the speechocean762 dataset, where Automatic Speech Recognition (ASR) model has been trained on the Librispeech dataset. The resultant mean error of the proposed approach is 3.38% and the value of the correlation coefficient achieved is 0.652.

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“…Several models may be used for acoustic modeling. The Hidden Markov Model (HMM) is a popular technique since it is effective for both training and recognition [9].…”

Section: ) Acoustic Modelingmentioning

confidence: 99%

Pronunciation Scoring With Goodness of Pronunciation and Dynamic Time Warping

Sheoran¹,

Bajgoti²,

Gupta³

et al. 2023

IEEE Access

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“…Training of the system requires creating a pattern representative for the features of class using one or more patterns that correspond to speech sounds of the same class. Many models are available for acoustic modeling out of them Hidden Markov Model (HMM) is widely used and accepted [12] as it is efficient algorithm for training and recognition. Many models or techniques are there for training the system as explained in section 5.…”

Section: Acoustic Modelingmentioning

confidence: 99%

A Review on Different Approaches for Speech Recognition System

Saksamudre¹,

Shrishrimal²,

Deshmukh³

2015

IJCA

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This paper presents the basic idea of speech recognition, proposed types of speech recognition, issues in speech recognition, different useful approaches for feature extraction of the speech signal with its advantage and disadvantage and various pattern matching approaches for recognizing the speech of the different speaker. Now day's research in speech recognition system is motivated for ASR system with a large vocabulary that supports speaker independent operations and continuous speech in different language.

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“…By recognitioning emotions of users add values in day to day life. Emotion recognition task is useful in day to day life in several ways like, lie detection system [2], audio/video retrieval [3,4], artificial intelligence and robotics, assign priority to customers in various call-centers, improved diagnostic tool, intelligent teaching/tutoring system, language conversion, improved computer games, smart car board system and sorting of voicemail/ messages. Such utilisations make emotion recognition from speech as best research topic in the field of speech processing.…”

Section: Introductionmentioning

confidence: 99%

Creation of speech corpus for emotion analysis in Gujarati language and its evaluation by various speech parameters

Tank

Hadia

2020

IJECE

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In the last couple of years emotion recognition has proven its significance in the area of artificial intelligence and man machine communication. Emotion recognition can be done using speech and image (facial expression), this paper deals with SER (speech emotion recognition) only. For emotion recognition emotional speech database is essential. In this paper we have proposed emotional database which is developed in Gujarati language, one of the official’s language of India. The proposed speech corpus bifurcate six emotional states as: sadness, surprise, anger, disgust, fear, happiness. To observe effect of different emotions, analysis of proposed Gujarati speech database is carried out using efficient speech parameters like pitch, energy and MFCC using MATLAB Software.

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Development of Application Specific Continuous Speech Recognition System in Hindi

Cited by 16 publications

References 10 publications

Pronunciation Scoring With Goodness of Pronunciation and Dynamic Time Warping

Pronunciation Scoring With Goodness of Pronunciation and Dynamic Time Warping

A Review on Different Approaches for Speech Recognition System

Creation of speech corpus for emotion analysis in Gujarati language and its evaluation by various speech parameters

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