Filled Pause Classification Using Energy-Boosted Mel-Frequency Cepstrum Coefficients

Hamzah, Raseeda; Jamil, Nursuriati; Seman, Noraini

doi:10.1007/978-981-4585-42-2_36

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…The YOHO database [47] contains a large scale, highquality speech corpus to support text-dependent speaker authentication research, such as is used in secure access technology. The data was collected in 1989 by ITT under a US Government contract.…”

Section: Results and Analysismentioning

confidence: 99%

Text-Independent Speaker Identification Using Vowel Formants

Almaadeed

Aggoun

Amira

2015

J Sign Process Syst

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Automatic speaker identification has become a challenging research problem due to its wide variety of applications. Neural networks and audio-visual identification systems can be very powerful, but they have limitations related to the number of speakers. The performance drops gradually as more and more users are registered with the system. This paper proposes a scalable algorithm for real-time text-independent speaker identification based on vowel recognition. Vowel formants are unique across different speakers and reflect the vocal tract information of a particular speaker. The contribution of this paper is the design of a scalable system based on vowel formant filters and a scoring scheme for classification of an unseen instance. Mel-Frequency Cepstral Coefficients (MFCC) and Linear Predictive Coding (LPC) have both been analysed for comparison to extract vowel formants by windowing the given signal. All formants are filtered by known formant frequencies to separate the vowel formants for further processing. The formant frequencies of each speaker are collected during the training phase. A test signal is also processed in the same way to find vowel formants and compare them with the saved vowel formants to identify the speaker for the current signal. A score-based scheme allows the speaker with the highest matching formants to own the current signal. This model requires less than 100 bytes of data to be saved for each speaker to be identified, and can identify the speaker within a second. Tests conducted on multiple databases show that this score-based scheme outperforms the back propagation neural network and Gaussian mixture models. Usually, the longer the speech files, the more significant were the improvements in accuracy.

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Section: Results and Analysismentioning

confidence: 99%

Text-Independent Speaker Identification Using Vowel Formants

Almaadeed

Aggoun

Amira

2015

J Sign Process Syst

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“…Some of the advanced NNs have the size of the input layer enlarged to two or three adjacent frames [33] in order to obtain a better context dependency for the acoustic feature vectors. The number of input layers can also be chosen by multiplying the cepstral order with the total frame number [34], leading to an extremely large input layer size. However, in both the above cases, the computational times are affected because of the increased number of hidden layers and states.…”

Section: Proposed Mnnmentioning

confidence: 99%

Speaker identification using multimodal neural networks and wavelet analysis

2015

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The rapid momentum of the technology progress in the recent years has led to a tremendous rise in the use of biometric authentication systems. The objective of this research is to investigate the problem of identifying a speaker from its voice regardless of the content. In this study, the authors designed and implemented a novel text-independent multimodal speaker identification system based on wavelet analysis and neural networks. Wavelet analysis comprises discrete wavelet transform, wavelet packet transform, wavelet sub-band coding and Mel-frequency cepstral coefficients (MFCCs). The learning module comprises general regressive, probabilistic and radial basis function neural networks, forming decisions through a majority voting scheme. The system was found to be competitive and it improved the identification rate by 15% as compared with the classical MFCC. In addition, it reduced the identification time by 40% as compared with the back-propagation neural network, Gaussian mixture model and principal component analysis. Performance tests conducted using the GRID database corpora have shown that this approach has faster identification time and greater accuracy compared with traditional approaches, and it is applicable to real-time, text-independent speaker identification systems

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Filled Pause Classification Using Energy-Boosted Mel-Frequency Cepstrum Coefficients

Cited by 2 publications

References 8 publications

Text-Independent Speaker Identification Using Vowel Formants

Text-Independent Speaker Identification Using Vowel Formants

Speaker identification using multimodal neural networks and wavelet analysis

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