Digital Speech Processing Using Matlab

Gopi, E. S.

doi:10.1007/978-81-322-1677-3

Cited by 18 publications

(13 citation statements)

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“…HMM is also a sequential generating probabilistic model, which means that the classifier acts on the assumption that neighboring frames are closely related. While this is valid for speech signal frames, there are better alternatives due to its assumption and algorithm complexity [19].…”

Section: Hidden Markov Model (Hmm)mentioning

confidence: 99%

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A Review on Emotion Recognition Algorithms using Speech Analysis

Gunawan

Alghifari

Morshidi

et al. 2018

IJEEI

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In recent years, there is a growing interest in speech emotion recognition (SER) by analyzing input speech. SER can be considered as simply pattern recognition task which includes features extraction, classifier, and speech emotion database. The objective of this paper is to provide a comprehensive review on various literature available on SER. Several audio features are available, including linear predictive coding coefficients (LPCC), Melfrequency cepstral coefficients (MFCC), and Teager energy based features. While for classifier, many algorithms are available including hidden Markov model (HMM), Gaussian mixture model (GMM), vector quantization (VQ), artificial neural networks (ANN), and deep neural networks (DNN). In this paper, we also reviewed various speech emotion database. Finally, recent related works on SER using DNN will be discussed.

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Section: Hidden Markov Model (Hmm)mentioning

confidence: 99%

“…The Gaussian mixture model (GMM) uses alternate generating probabilistic model, which implies that for a particular word we can form multivariate Gaussian density models that represents all the frames [19]. Similar to HMM, GMM can be expressed in mathematical terms.…”

Section: Gaussian Mixture Models (Gmm)mentioning

confidence: 99%

A Review on Emotion Recognition Algorithms using Speech Analysis

Gunawan

Alghifari

Morshidi

et al. 2018

IJEEI

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“…Speaker identification is one important application of biometrics and forensics to identify speakers based on their unique voice pattern [1][2][3]. According to [4], feature extraction within speaker identification should be less influenced by noise or the person's health.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a speaker identification system with and without fusion using three databases in the presence of noise and handset effects

Al-Kaltakchi

Woo

Dlay

et al. 2017

EURASIP J. Adv. Signal Process.

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In this study, a speaker identification system is considered consisting of a feature extraction stage which utilizes both power normalized cepstral coefficients (PNCCs) and Mel frequency cepstral coefficients (MFCC). Normalization is applied by employing cepstral mean and variance normalization (CMVN) and feature warping (FW), together with acoustic modeling using a Gaussian mixture model-universal background model (GMM-UBM). The main contributions are comprehensive evaluations of the effect of both additive white Gaussian noise (AWGN) and non-stationary noise (NSN) (with and without a G.712 type handset) upon identification performance. In particular, three NSN types with varying signal to noise ratios (SNRs) were tested corresponding to street traffic, a bus interior, and a crowded talking environment. The performance evaluation also considered the effect of late fusion techniques based on score fusion, namely, mean, maximum, and linear weighted sum fusion. The databases employed were TIMIT, SITW, and NIST 2008; and 120 speakers were selected from each database to yield 3600 speech utterances. As recommendations from the study, mean fusion is found to yield overall best performance in terms of speaker identification accuracy (SIA) with noisy speech, whereas linear weighted sum fusion is overall best for original database recordings.

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“…Formants are often measured as amplitude peaks in the frequency spectrum of the sound using a spectrogram or a spectrum analyzer and, in the case of the voice, this gives an estimate of the vocal tract resonances. In vowels spoken with a high fundamental frequency fundamental frequency, as in a female or child voice, however, the frequency of the resonance may lie between the widely spaced harmonics and hence no corresponding peak is visible [6,7].…”

Section: Introductionmentioning

confidence: 99%

Speaker Independent Speech Recognition of Isolated Words in Room Environment

Tabassum¹,

Jahan²,

Rahman³

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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In this paper, the process of recognizing some important words from a large set of vocabularies is demonstrated based on the combination of dynamic and instantaneous features of the speech spectrum. There are many procedures to recognize a word by its vowel, but this paper presents the highly effective speaker independent speech recognition in a typical room environment noise cases. To distinguish several isolated words of the sound of different vowels, two important features such as Pitch and Formant are extracted from the speech signals collected from a number of random male and female speakers. The extracted features are then analyzed for the particular utterances to train the system. The specific objectives of this work are to implement an isolated and automatic word speech recognizer, which is capable of recognizing as well as responding to speech and an audio interfacing system between human and machine for an effective human-machine interaction. The whole system has been tested using computer codes, and the result was satisfactory in almost 90% of cases. However, the system might get confused by similar vowel sounds sometimes.

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Digital Speech Processing Using Matlab

Cited by 18 publications

References 0 publications

A Review on Emotion Recognition Algorithms using Speech Analysis

A Review on Emotion Recognition Algorithms using Speech Analysis

Evaluation of a speaker identification system with and without fusion using three databases in the presence of noise and handset effects

Speaker Independent Speech Recognition of Isolated Words in Room Environment

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