MFCC-GMM based accent recognition system for Telugu speech signals

Mannepalli, Kasiprasad; Sastry, Panyam Narahari; Maloji, Suman

doi:10.1007/s10772-015-9328-y

Cited by 51 publications

(25 citation statements)

References 15 publications

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“…MFCC-GMM based accent recognition system for Malayalam speech signals in MATLAB and Raspberry Pi is used for the implementation. Mel Frequency Cepstral Coefficients (MFCC) [1] are the features used for classification whereas GMM classifier is used for accent recognition.…”

Section: Methodsmentioning

confidence: 99%

Domestic Language Accent Detector Using MFCC and GMM

K¹,

Joseph²

2020

International Journal of Applied Engineering Research

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Section: Methodsmentioning

confidence: 99%

Domestic Language Accent Detector Using MFCC and GMM

K¹,

Joseph²

2020

International Journal of Applied Engineering Research

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“…To nicely deal with the important challenges such as distinct emotions, low quality recording, and independent affective states, DBN is combined with Fractional Calculus to extract discriminative features [32]. Besides, multitask learning is applied to leverage activation and valence information for acoustic emotion recognition using DBN framework [33].…”

Section: Related Workmentioning

confidence: 99%

Random Deep Belief Networks for Recognizing Emotions from Speech Signals

Wen

Huang

et al. 2017

Computational Intelligence and Neuroscience

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Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN) can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN) method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces. Each random subspace is then provided for DBN to yield the higher level features as the input of the classifier to output an emotion label. All outputted emotion labels are then fused through the majority voting to decide the final emotion label for the input speech signal. The conducted experimental results on benchmark speech emotion databases show that RDBN has better accuracy than the compared methods for speech emotion recognition.

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“…Although this model presented some brilliant results regarding speech or speaker recognition techniques [6], [7], [8], [9]. However, its well known that some phenomena can not be captured by this model [10].…”

Section: Am-fm Modulation Featurementioning

confidence: 99%

Modulation Components and Genetic Algorithm for Speaker Recognition System

Hassan¹,

Ajel²,

Ibrahim³

2017

ijacsa

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Abstract-In this paper, the aim is to investigate weather or not that changing the filter-bank components (of the speaker recognition system) could improve the system performance in identifying the speaker. The filter is composed of 30 Gamatone filter channels. First, the channels are mel distributed of the frequency line. Then the componentsvalues (center frequencies and bandwidths) changes with each run. Genetic algorithm (GA) is adopted to improve the filter component values that, in a result, improve the system performance. At each GA run, a new set of filter components will be generated that aimed to improve the performance comparing with the previous run. This will continue until the system reach to the maximum accuracy or the GA reach to its limits. Results show that the system will be improved at each run, however, different words might response differently to the system filter changing. Also, in terms of additive noise, the results show that although the digits affected differently by the noise, the system still get improving with reach GA run.

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MFCC-GMM based accent recognition system for Telugu speech signals

Cited by 51 publications

References 15 publications

Domestic Language Accent Detector Using MFCC and GMM

Domestic Language Accent Detector Using MFCC and GMM

Random Deep Belief Networks for Recognizing Emotions from Speech Signals

Modulation Components and Genetic Algorithm for Speaker Recognition System

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