A quantitative assessment of the relative speaker discriminating properties of phonemes

Eatock, Julian P.; Mason, John S.

doi:10.1109/icassp.1994.389337

Cited by 48 publications

(30 citation statements)

References 4 publications

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“…However, we need to fix the length of the test segment to be equal for all classes for fair comparison. Previous studies have fixed the length of the test segment [1], [3] or showed the results as an average likelihood versus the number of frames in each phoneme [2] for a similar reason. Thus, we also perform speaker identification test with a fixed test length.…”

Section: Controlled Condition: Same Test Lengthmentioning

confidence: 99%

“…We can estimate that if the amount of transitions is similar to the amount of vowels, transitions may achieve a better result than vowels. Nasals, which are well-known as a good feature for speaker recognition [1] are the third but its error rate is much worse than vowels and transitions. This is because the total length of the nasal tokens in an ordinary sentence is much less than the length of vowels.…”

Section: Practical Condition: One Test Sentencementioning

confidence: 99%

“…Fundamentally, they use the fact that some classes of phonemes, such as vowels and nasals, include more speaker-related information than obstruents such as stops, affricatives, and fricatives [1]. One of the approaches assigns different weights to each phoneme depending on how much each phoneme includes speaker related information [2].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On the Importance of Transition Regions for Automatic Speaker Recognition

Lee

Jung

Choi

et al. 2010

IEICE Trans. Inf. & Syst.

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SUMMARYThis letter describes the importance of transition regions, e.g. at phoneme boundaries, for automatic speaker recognition compared with using steady-state regions. Experimental results of automatic speaker identification tasks confirm that transition regions include the most speaker distinctive features. A possible reason for obtaining such results is described in view of articulation, in particular, the degree of freedom of articulators. These results are expected to provide useful information in designing an efficient automatic speaker recognition system.

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Section: Controlled Condition: Same Test Lengthmentioning

confidence: 99%

Section: Practical Condition: One Test Sentencementioning

confidence: 99%

See 1 more Smart Citation

On the Importance of Transition Regions for Automatic Speaker Recognition

Lee

Jung

Choi

et al. 2010

IEICE Trans. Inf. & Syst.

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show abstract

“…Various approaches have been proposed to overcome this limitation by utilizing phoneme information. [4][5][6][7] In this paper, we also propose a method that utilizes phoneme information to improve speaker recognition performance. While previous studies focus on using separate models for each phoneme and combining scores, 4,6,7 we focus on finding an optimal phoneme class ratio, the portion of each phoneme class in an utterance, that maximizes speaker recognition performance based on mutual information.…”

Section: Introductionmentioning

confidence: 99%

Phonetically optimized speaker modeling for robust speaker recognition

Lee

Choi

Kang

2009

The Journal of the Acoustical Society of America

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This paper proposes an efficient method to improve speaker recognition performance by dynamically controlling the ratio of phoneme class information. It utilizes the fact that each phoneme contains different amounts of speaker discriminative information that can be measured by mutual information. After classifying phonemes into five classes, the optimal ratio of each class in both training and testing processes is adjusted using a non-linear optimization technique, i.e., the Nelder–Mead method. Speaker identification results verify that the proposed method achieves 18% improvement in terms of error rate compared to a baseline system.

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“…One of a number of studies [5] indicated that not all phonemes are equally discriminative, but in addition, this also implies that not all areas of the acoustic feature space are equally discriminative. The use of a secondary classifier presents a simple solution for evaluating the weight of evidence from the speaker's score and the consideration of the discriminative power of each partitioned region of the feature space.…”

Section: Secondary Classificationmentioning

confidence: 99%

Secondary Classification for GMM Based Speaker Recognition

Pelecanos

Povey

Ramaswamy

2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings

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This paper discusses the use of a secondary classifier to reweight the frame-based scores of a speaker recognition system according to which region in feature space they belong. The score mapping function is constructed to perform a likelihood ratio (LR) correction of the original LR scores. This approach has the ability to limit the effect of rogue model components and regions of feature space that may not be robust to different audio environments, handset types or speakers.Prior information available from tests on a development data set can be used to determine a log-likelihood-ratio mapping function that more appropriately weights each speech frame. The computational overhead for this approach in online mode is close to negligible for significant performance gains shown for the NIST 2004 Speaker Recognition Evaluation data.

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A quantitative assessment of the relative speaker discriminating properties of phonemes

Cited by 48 publications

References 4 publications

On the Importance of Transition Regions for Automatic Speaker Recognition

On the Importance of Transition Regions for Automatic Speaker Recognition

Phonetically optimized speaker modeling for robust speaker recognition

Secondary Classification for GMM Based Speaker Recognition

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