Application of the modified group delay function to speaker identification and discrimination

Hegde, Rajesh M.; Murthy, Hema A.; Rao, G. V. R. Seshagiri

doi:10.1109/icassp.2004.1326036

Cited by 49 publications

(23 citation statements)

References 9 publications

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“…Usually only the magnitude spectrum is retained, based on the belief that phase has little perceptual importance. However, [179] provides opposing evidence while [96] described a technique which utilizes phase information.…”

Section: Short-term Spectral Featuresmentioning

confidence: 99%

An overview of text-independent speaker recognition: From features to supervectors

Kinnunen¹,

Li²

2010

Speech Communication

1,240

706

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This paper gives an overview of automatic speaker recognition technology, with an emphasis on text-independent recognition. Speaker recognition has been studied actively for several decades. We give an overview of both the classical and the state-of-the-art methods. We start with the fundamentals of automatic speaker recognition, concerning feature extraction and speaker modeling. We elaborate advanced computational techniques to address robustness and session variability. The recent progress from vectors towards supervectors opens up a new area of exploration and represents a technology trend. We also provide an overview of this recent development and discuss the evaluation methodology of speaker recognition systems. We conclude the paper with discussion on future directions.

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Section: Short-term Spectral Featuresmentioning

confidence: 99%

An overview of text-independent speaker recognition: From features to supervectors

Kinnunen¹,

Li²

2010

Speech Communication

1,240

706

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show abstract

“…Due to the fact that these features are relatively unstable, and it is difficult to separate the phase information accurately, there has been little progress until recently. Robust ASR and Speaker Identification have been recent advances, where a Modified Group Delay Function (MODGDF) is used [17,18]. MODGDF has been tested in Language Identification but did not show a significant or stable performance improvement [17].…”

Section: Phase-based Featuresmentioning

confidence: 99%

Language-Dependent Contribution Measuring and Weighting for Combining Likelihood Scores in Language Identification Systems

Yin

Ambikairajah

Chen

2008

J Sign Process Syst Sign Image Video Technol

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Developing a fusion-based system is one of the key research issues in modern Language Identification (LID) systems. In this paper we investigate existing fusion techniques for LID systems and propose an alternative solution. By directly utilizing language-dependent contribution information, a novel Language-Dependent Weighting approach is introduced and implemented. We investigate various contribution measures, including LID performances, likelihood ratios, and Kullback-Leibler divergence. These measures are conducted from either development datasets or class models. The advantage of using language-dependent weighting over language-independent weighting is illustrated using a Language-Dependent Contribution Map. Both the OGI and CallFriend databases show a very similar contribution pattern which is related to language characteristics. Experiments on the NIST LRE 2003 task and OGI database demonstrate that the proposed fusion technique outperforms other recent fusion techniques when the amount of available development data is limited. In particular, the system based on Kullback-Leibler divergence achieved the best performance while eliminating the need for development data.

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“…It has been used in speaker identification (Hegde et al [84]), but also in speech analysis, speech segmentation, speech recognition and language identification frameworks (Murthy and Yegnanarayana [85]). …”

Section: Stft-based Frequency Featuresmentioning

confidence: 99%

A Review of Physical and Perceptual Feature Extraction Techniques for Speech, Music and Environmental Sounds

2016

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Endowing machines with sensing capabilities similar to those of humans is a prevalent quest in engineering and computer science. In the pursuit of making computers sense their surroundings, a huge effort has been conducted to allow machines and computers to acquire, process, analyze and understand their environment in a human-like way. Focusing on the sense of hearing, the ability of computers to sense their acoustic environment as humans do goes by the name of machine hearing. To achieve this ambitious aim, the representation of the audio signal is of paramount importance. In this paper, we present an up-to-date review of the most relevant audio feature extraction techniques developed to analyze the most usual audio signals: speech, music and environmental sounds. Besides revisiting classic approaches for completeness, we include the latest advances in the field based on new domains of analysis together with novel bio-inspired proposals. These approaches are described following a taxonomy that organizes them according to their physical or perceptual basis, being subsequently divided depending on the domain of computation (time, frequency, wavelet, image-based, cepstral, or other domains). The description of the approaches is accompanied with recent examples of their application to machine hearing related problems.

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Application of the modified group delay function to speaker identification and discrimination

Cited by 49 publications

References 9 publications

An overview of text-independent speaker recognition: From features to supervectors

An overview of text-independent speaker recognition: From features to supervectors

Language-Dependent Contribution Measuring and Weighting for Combining Likelihood Scores in Language Identification Systems

A Review of Physical and Perceptual Feature Extraction Techniques for Speech, Music and Environmental Sounds

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