The NIST speaker recognition evaluation – Overview, methodology, systems, results, perspective

Reynolds, Douglas A.; Doddington, George R.; Przybocki, Mark A.; Martín, Alvin F.

doi:10.1016/s0167-6393(99)00080-1

Cited by 284 publications

(146 citation statements)

References 10 publications

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“…As such, EER is not suitable for evaluating the actual decision performance. An a priori threshold can be drawn by evaluating the detection cost function (DCF, Doddington et al, 2000) which is defined as a weighted sum of the Miss and False Alarm probabilities:…”

Section: Actual Decision Performance Of Classifiermentioning

confidence: 99%

Automatic discrimination between laughter and speech

Truong¹,

Leeuwen²

2007

Speech Communication

131

118

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Emotions can be recognized by audible paralinguistic cues in speech. By detecting these paralinguistic cues that can consist of laughter, a trembling voice, coughs, changes in the intonation contour etc., information about the speaker's state and emotion can be revealed. This paper describes the development of a gender-independent laugh detector with the aim to enable automatic emotion recognition. Different types of features (spectral, prosodic) for laughter detection were investigated using different classification techniques (Gaussian Mixture Models, Support Vector Machines, Multi Layer Perceptron) often used in language and speaker recognition. Classification experiments were carried out with short pre-segmented speech and laughter segments extracted from the ICSI Meeting Recorder Corpus (with a mean duration of approximately 2 s). Equal error rates of around 3% were obtained when tested on speaker-independent speech data. We found that a fusion between classifiers based on Gaussian Mixture Models and classifiers based on Support Vector Machines increases discriminative power. We also found that a fusion between classifiers that use spectral features and classifiers that use prosodic information usually increases the performance for discrimination between laughter and speech. Our acoustic measurements showed differences between laughter and speech in mean pitch and in the ratio of the durations of unvoiced to voiced portions, which indicate that these prosodic features are indeed useful for discrimination between laughter and speech.

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Section: Actual Decision Performance Of Classifiermentioning

confidence: 99%

Automatic discrimination between laughter and speech

Truong¹,

Leeuwen²

2007

Speech Communication

131

118

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“…For speaker recognition tasks, numerous speech features and modeling techniques have been proposed over the years [5,6]. However, it is still difficult to implement a single classifier that exhibits sufficiently high performance in a multilingual environment.…”

Section: Introductionmentioning

confidence: 99%

Combination of Classifiers Decisions for Multilingual Speaker Identification

Nagaraja¹,

Jayanna²

2015

J Inf Process Syst

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State-of-the-art speaker recognition systems may work better for the English language. However, if the same system is used for recognizing those who speak different languages, the systems may yield a poor performance. In this work, the decisions of a Gaussian mixture model-universal background model (GMM-UBM) and a learning vector quantization (LVQ) are combined to improve the recognition performance of a multilingual speaker identification system. The difference between these classifiers is in their modeling techniques. The former one is based on probabilistic approach and the latter one is based on the fine-tuning of neurons. Since the approaches are different, each modeling technique identifies different sets of speakers for the same database set. Therefore, the decisions of the classifiers may be used to improve the performance. In this study, multitaper mel-frequency cepstral coefficients (MFCCs) are used as the features and the monolingual and cross-lingual speaker identification studies are conducted using NIST-2003 and our own database. The experimental results show that the combined system improves the performance by nearly 10% compared with that of the individual classifier.

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“…In this test case, the adaptive MCS is composed of an ensemble of 2-class Probabilistic Fuzzy ARTMAP (PFAM) classifiers for each enrolled subject. Average performance is presented and Doddington zoo [13] analysis is employed to compare individual-specific parameters for LTM management. Using the menagerie terminology introduced in [12], this analysis allows to categorize subjects into 4 groups: sheep, goat, wolf and lamb-like individuals according to their performance.…”

Section: Introductionmentioning

confidence: 99%

An Individual-Specific Strategy for Management of Reference Data in Adaptive Ensembles for Person Re-Identification

De-la-Torre¹,

Granger²,

Sabourin³

et al. 2013

5th International Conference on Imaging for Crime Detection and Prevention (ICDP 2013)

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In video surveillance, person re-identification refers to recognizing individuals of interest from faces captured across a network of video cameras. Face recognition in such applications is challenging because faces are captured with limited spatial and temporal constraints. In addition, facial models for recognition are commonly designed using limited reference samples from faces captured under specific conditions. Given new reference samples, updating facial models may allow maintaining a high level of performance over time. Although adaptive ensembles have been successfully applied to robust modeling of an individual's face, reference data samples must be stored for validation. In this paper, a memory management strategy based on Kullback-Leiber (KL) divergence is proposed to rank and select the most relevant validation samples over time in adaptive individual-specific ensembles. When new reference data becomes available for an individual, updates to the corresponding ensembles are validated using a mixture of new and previously-stored samples. Only the samples with the highest KL divergence are preserved in memory for future adaptations. The strategy is compared with reference classifiers using videos from the FIA data set. Simulation results show that the proposed strategy tends to select samples of statistically different subjects (so-called "wolfs") for validation, thereby reducing the number of samples per individual by up to 80%, yet maintaining a high level of performance.

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The NIST speaker recognition evaluation – Overview, methodology, systems, results, perspective

Cited by 284 publications

References 10 publications

Automatic discrimination between laughter and speech

Automatic discrimination between laughter and speech

Combination of Classifiers Decisions for Multilingual Speaker Identification

An Individual-Specific Strategy for Management of Reference Data in Adaptive Ensembles for Person Re-Identification

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