Robust speaker clustering in eigenspace

Faltlhauser, Robert; Ruske, Günther

doi:10.1109/asru.2001.1034588

Cited by 12 publications

(14 citation statements)

References 11 publications

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“…Many researches have been done for multilingual speaker recognition system using ANN, and they have used different model like Artificial Neural Networks (ANNs), Hidden Markov Model (HMMs), Harmonic Product Spectrum (HPS) [11,12,13]. In pattern classification or recognition phase, there are various methods used as vector quantization technique from signal processing to store features in codebooks [12,14].…”

Section: State Of the Artmentioning

confidence: 99%

Multilingual speaker recognition using ANFIS

Pandey

Ranjan

Kumar

et al. 2010

2010 2nd International Conference on Signal Processing Systems

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Feature based Recognition Systems has been an area of intense research for long. The creation of a reliable, robust and sufficiently efficient recognition system has been tried using features from several sources including textual and image sources. Speech based sources have also been used for the creation of such a recognition system. However, variations caused due to differences in individual speaker characteristics, mood variations and inter-mingled noise disturbances make the realization of such a system very difficult. This paper proposes a recognition system for identification of the speaker, language and the words spoken. The system makes use of Adaptive Neuro-Fuzzy Inference paradigm for the same. First, the sampling frequency and the speech features are extracted from the speech database to form speech feature vectors. The features used are LPC, LPCC, RC, LAR, LSF and ARSCIN. The speech database is prepared using 25 speakers including male and female speakers. Five different speaking texts of different languages having same meaning are used to get the best speaker identification accuracy. The languages spoken by the speakers include English, Hindi, Punjabi, Sanskrit and Telugu. The Feature vectors, thus prepared, are fed to an Adaptive Neuro-Fuzzy Inference System for speaker, language and word recognition. The experimental results show the system to be amply efficient and successful in the recognition tasks involved.

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Section: State Of the Artmentioning

confidence: 99%

Multilingual speaker recognition using ANFIS

Pandey

Ranjan

Kumar

et al. 2010

2010 2nd International Conference on Signal Processing Systems

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“…The most popular speaker-clustering method employs hierarchical agglomerative clustering (HAC) (Gish et al, 1991;Jin et al, 1997;Solomonoff et al, 1998;Chen and Gopalakrishnan, 1998;Reynolds et al, 1998;Johnson and Woodland, 1998;Faltlhauser and Ruske, 2001;Ajmera et al, 2002;Moh et al, 2003;Liu, 2005). This approach generates a cluster tree by sequentially merging the utterances deemed similar to each other.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary minimization of the Rand index for speaker clustering

Tsai

Wang

2009

Computer Speech & Language

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We propose an effective method for clustering unknown speech utterances based on their associated speakers. The method jointly optimizes the generated clusters and the required number of clusters by estimating and minimizing the Rand index. The metric reflects the clustering errors that arise when utterances from the same speaker are placed in different clusters; or when utterances from different speakers are placed in the same cluster. One useful characteristic of the Rand index is that its value only reaches the minimum when the number of clusters is equal to the size of the true speaker population. We approximate the Rand index by a function of the similarity measures between utterances and then use a genetic algorithm to determine the cluster in which each utterance should be located, such that the function is minimized. Our experiment results show that this novel speaker-clustering method outperforms conventional methods that use the Bayesian information criterion to determine the required number of clusters.

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“…Since the voice data of new speakers is only used for computing coordinates, the eigenvoice technique has proven particularly effective for speaker adaptation in terms of computational efficiency and the requirements of adaptation data. The technique has also been applied to cluster speakers to improve speechrecognition performance [13]. In contrast to the work in [13], which relies on a set of extra speech data to construct the eigenvoice space, the proposed method fully utilizes the data from the utterances to be clustered.…”

Section: Eigenvoice-motivated Reference Spacementioning

confidence: 99%

“…The technique has also been applied to cluster speakers to improve speechrecognition performance [13]. In contrast to the work in [13], which relies on a set of extra speech data to construct the eigenvoice space, the proposed method fully utilizes the data from the utterances to be clustered.…”

Section: Eigenvoice-motivated Reference Spacementioning

confidence: 99%

Automatic Speaker Clustering Using a Voice Characteristic Reference Space and Maximum Purity Estimation

Tsai

Cheng

Wang

2007

IEEE Trans. Audio Speech Lang. Process.

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This paper investigates the problem of automatically grouping unknown speech utterances based on their associated speakers. In attempts to determine which utterances should be grouped together, it is necessary to measure the voice similarities between utterances. Since most existing methods measure the inter-utterance similarities based directly on the spectrum-based features, the resulting clusters may not be well-related to speakers, but to various acoustic classes instead.This study remedies this shortcoming by projecting utterances onto a reference space trained to cover the generic voice characteristics underlying the whole utterance collection. The resultant projection vectors naturally reflect the relationships of voice similarities among all the utterances, and hence are more robust against interference from non-speaker factors. Then, a clustering method based on maximum purity estimation is proposed, with the aim of maximizing the similarities between utterances within all the clusters. This method employs a genetic algorithm to determine the cluster to which each utterance should be assigned, which overcomes the limitation of conventional hierarchical clustering that the final result can only reach the local optimum. The proposed clustering method also incorporates a Bayesian information criterion to determine how many clusters should be created.

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Robust speaker clustering in eigenspace

Cited by 12 publications

References 11 publications

Multilingual speaker recognition using ANFIS

Multilingual speaker recognition using ANFIS

Evolutionary minimization of the Rand index for speaker clustering

Automatic Speaker Clustering Using a Voice Characteristic Reference Space and Maximum Purity Estimation

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