Robust speech/non-speech detection using LDA applied to MFCC

Martin, Arnaud; Charlet, Delphine; Mauuary, Laurent

doi:10.1109/icassp.2001.940811

Cited by 63 publications

(30 citation statements)

References 4 publications

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“…After acquisition of speech utterances, wiener filter has been used to remove the background noise from the original speech utterances [16,17,18]. Speech end points detection and silence part removal algorithm has been used to detect the presence of speech and to remove pulse and silences in a background noise [19,20,21,22,23]. To detect word boundary, the frame energy is computed using the sort-term log energy equation [24] Pre-emphasis has been used to balance the spectrum of voiced sounds that have a steep roll-off in the high frequency region [25,26,27].…”

Section: Speech Signal Processing For Speaker Identificationmentioning

confidence: 99%

Noise Robust Speaker Identification using PCA based Genetic Algorithm

Islam¹,

Rahman²

2010

IJCA

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This paper emphasizes text dependent speaker identification system on Principal Component Analysis based Genetic Algorithm which deals with detecting a particular speaker from a known population under noisy environment. At first, the system prompts the user to get speech utterance. Noises are eliminated from the speech utterances by using wiener filtering technique. To extract the features from the speech, various types of feature extraction techniques such as RCC, LPCC, MFCC, MFCC and MFCC have been used. Principal Component Analysis has been used to reduce the dimensionality of the speech feature vector. To classify the speech utterances, Genetic Algorithm has been used. NOIZEOUS speech database has been used to measure the performance of this system under the condition of various SNRs. Experimental results show the superiority of the proposed close-set text dependent speaker identification system which can be used for security and access control purposes.

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Section: Speech Signal Processing For Speaker Identificationmentioning

confidence: 99%

Noise Robust Speaker Identification using PCA based Genetic Algorithm

Islam¹,

Rahman²

2010

IJCA

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“…After acquisition of speech utterances, winner filter has been used to remove the background noise from the original speech utterances [22,23,24]. Speech end points detection and silence part removal algorithm has been used to detect the presence of speech and to remove pulse and silences in a background noise [25,26,27,28,29]. To detect word boundary, the frame energy is computed using the sort-term log energy equation [24], (1) Pre-emphasis has been used to balance the spectrum of voiced sounds that have a steep roll-off in the high frequency region [30,31,32].…”

Section: Audio Identificationmentioning

confidence: 99%

Likelihood Ratio Based Score Fusion for Audio-Visual Speaker Identification in Challenging Environment

Islam¹,

Rahman²

2010

IJCA

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It is well known to enhance the performance of noise robust speaker identification using visual speech information with audio utterances. This paper presents an approach to evaluate the performance of a noise robust audio-visual speaker identification system using likelihood ratio based score fusion in challenging environment. Though the traditional HMM based audio-visual speaker identification system is very sensitive to the speech parameter variation, the proposed likelihood ratio based score fusion method is found to be stance and performs well for improving the robustness and naturalness of human-computerinteraction. In this paper, we investigate the proposed audiovisual speaker identification system in typical office environments conditions. To do this, we investigated two approaches that utilize speech utterance with visual features to improve speaker identification performance in acoustically and visually challenging environment: one seeks to eliminate the noise from the acoustic and visual features by using speech and facial image pre-processing techniques. The other task combines speech and facial features that have been used by the multiple Discrete Hidden Markov Model classifiers with likelihood ratio based score fusion. It is shown that the proposed system can improve a significant amount of performance for audio-visual speaker identification in challenging official environment conditions.

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“…Silence removal is a well known technique adopted for many years for this and also for dimensionality reduction in speech that facilitates the system to be computationally more efficient. This type of classification of speech into voiced or silence/unvoiced sounds [2] finds other applications mainly in fundamental frequency estimation, formant extraction or syllable marking and so on.…”

Section: Introductionmentioning

confidence: 99%

Voicing Detection in Noisy Speech Signal

Bouzid

Ellouze

2008

Lecture Notes in Computer Science

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Abstract. An algorithm for voicing detection in noisy speech signal is proposed. This algorithm is based on the product of wavelet transforms at some scales called multi-scale product. The multi-scale product has the ability to reinforce the edge in the signal while suppressing additive noise. Motivated by the fact that unvoiced sounds are, in most important speech production models, considered as filtered noise, we apply the multi-scale product on speech signal for detection of voiced segments. In fact, the multi-scale product anneals the signal frames corresponding to unvoiced sounds and frames of silence, while it conserves speech periodicity for voiced frames.

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Robust speech/non-speech detection using LDA applied to MFCC

Cited by 63 publications

References 4 publications

Noise Robust Speaker Identification using PCA based Genetic Algorithm

Noise Robust Speaker Identification using PCA based Genetic Algorithm

Likelihood Ratio Based Score Fusion for Audio-Visual Speaker Identification in Challenging Environment

Voicing Detection in Noisy Speech Signal

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