Analysis and Synthesis of Vowels Using Matlab

Nica, A.; Caruntu, Alexandru; Toderean, Gavril; Buza, Ovidiu

doi:10.1109/aqtr.2006.254662

Cited by 7 publications

(3 citation statements)

References 3 publications

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“…It is used for formant analysis [9]. LPC is one of the most powerful speech analysis techniques and it has gained popularity as a formant estimation technique [10].…”

Section: Linear Predictive Codes (Lpc)mentioning

confidence: 99%

Performance Analysis of Lip Synchronization Using LPC, MFCC and PLP Speech Parameters

Goyani¹,

Dave²,

Patel³

2010

2010 International Conference on Computational Intelligence and Communication Networks

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Many multimedia applications and entertainment industry products like games, cartoons and film dubbing require speech driven face animation and audio-video synchronization. Only Automatic Speech Recognition system (ASR) does not give good results in noisy environment. Audio Visual Speech Recognition system plays vital role in such harsh environment as it uses both -audio and visual -information. In this paper, we have proposed a novel approach with enhanced performance over traditional methods that have been reported so far. Our algorithm works on the bases of acoustic and visual parameters to achieve better results. We have tested our system for English language using LPC, MFCC and PLP parameters of the speech. Lip parameters like lip width, lip height etc are extracted from the video and these both acoustic and visual parameters are used to train systems like Artificial Neural Network (ANN), Vector Quantization (VQ), Dynamic Time Warping (DTW), Support Vector Machine (SVM). We have employed neural network in our research work with LPC, MFCC and PLP parameters. Results show that our system is giving very good response against tested vowels.

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“…It is used for formant analysis [9]. LPC is one of the most powerful speech analysis techniques and it has gained popularity as a formant estimation technique [10].…”

Section: Linear Predictive Codes (Lpc)mentioning

confidence: 99%

Performance Analysis of Lip Synchronization Using LPC, MFCC and PLP Speech Parameters

Goyani¹,

Dave²,

Patel³

2010

2010 International Conference on Computational Intelligence and Communication Networks

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“…Paul et al presented time domain analysis for energy and zero crossing rate (ZCR), cepstral analysis for fundamental frequency,Linear Predictive coding (LPC) for formants and also synthesized it using MATLAB [3]. Nica et al designed Bangla speech recognition system using LPC and Artificial Neural Network [4]. Ning used inverse of spectral features called cepstral for word recognition [5].…”

Section: Introductionmentioning

confidence: 99%

Word recognition from speech signal using linear predictive coding and spectrum analysis

Singh

2018

IJET

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This paper presents a technique for isolated word recognition from speech signal using Spectrum Analysis and Linear Predictive Coding (LPC). In the present study, only those words have been analyzed which are commonly used during a telephonic conversations by criminals. Since each word is characterized by unique frequency spectrum signature, thus, spectrum analysis of a speech signal has been done using certain statistical parameters. These parameters help in recognizing a particular word from a speech signal, as there is a unique value of a feature for each word, which helps in distinguishing one word from the other. Second method used is based on LPC coefficients. Analysis of features extracted using LPC coefficients help in identification of a specific word from the input speech signal. Finally, a combination of best features from these two methods has been used and a hybrid technique is proposed. An accuracy of 94% has been achieved for sample size of 400 speech words.

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“…[2] These Devnagari vowels speech signals are estimated in noisy environment (original signals) for analysis and synthesis. The original speech signals are unbalanced to adjustment of a pitch interval with help of some feature extraction techniques.…”

Section: Introductionmentioning

confidence: 99%

Pitch Estimation of Devnagari Vowels using Cepstral and AutocorrelationTechniques for Original Speech Signal

Nimbhore¹,

Ramteke²,

Ramteke³

2012

IJCA

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This paper deals with pitch estimation of spoken Devnagari vowels from the original speech signals. Devnagari vowels are playing the vital role in pronunciation of any word. Each vowel is classified as starting, middle and end according to the duration of occurrences in the word. The Devnagari script having 12-vowels and 34-consonants are used in some Indian language like Marathi. The Devnagari vowels are categorised into 5-types such as short vowels, long vowels, conjunct vowels, nasal vowel and visarg vowel. The Pitch frequency is estimated from the features of speech signals via pitch detection algorithm through autocorrelation and cepstral methods. These vowels are recorded through PRAAT tools with noisy environment. The pitch estimation of original pitch frequency hasbeen calculated in statistical manner (Mean and standard deviation). The implementation, experiments and result discussions are also existence.These results which have been appropriate match with both techniques.

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Analysis and Synthesis of Vowels Using Matlab

Cited by 7 publications

References 3 publications

Performance Analysis of Lip Synchronization Using LPC, MFCC and PLP Speech Parameters

Performance Analysis of Lip Synchronization Using LPC, MFCC and PLP Speech Parameters

Word recognition from speech signal using linear predictive coding and spectrum analysis

Pitch Estimation of Devnagari Vowels using Cepstral and AutocorrelationTechniques for Original Speech Signal

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