Speaker verification over long distance telephone lines

Naik, Jayant M.; Netsch, Lorin; Doddington, George R.

doi:10.1109/icassp.1989.266479

Cited by 50 publications

(16 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Matching is usually done by evaluating the likelihood of the test utterance with respect to the model. The Gaussian mixture model (GMM) [198,197] and the hidden Markov model (HMM) [19,171] are the most popular models for text-independent and textdependent recognition, respectively.…”

Section: Speaker Modelingmentioning

confidence: 99%

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

Kinnunen¹,

Li²

2010

Speech Communication

1,217

706

View full text Add to dashboard Cite

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.

show abstract

Section: Speaker Modelingmentioning

confidence: 99%

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

Kinnunen¹,

Li²

2010

Speech Communication

1,217

706

View full text Add to dashboard Cite

show abstract

“…Speaker recognition systems based on an HMM architecture used speaker models derived from a multi-word sentence, a single word, or a phoneme. Typically, multi-word phrases (a string of seven to ten digits, for example) were used, and models for each individual word and for "silence" were combined at a sentence level according to a predefined sentence-level grammar [34]. (4) VQ/HMM-based text-independent methods: Nonparametric and parametric probability models were investigated for text-independent speaker recognition.…”

Section: Smentioning

confidence: 99%

40 Years of Progress in Automatic Speaker Recognition

Furui

2009

Advances in Biometrics

View full text Add to dashboard Cite

Abstract. Research in automatic speaker recognition has now spanned four decades. This paper surveys the major themes and advances made in the past 40 years of research so as to provide a technological perspective and an appreciation of the fundamental progress that has been accomplished in this important area of speech-based human biometrics. Although many techniques have been developed, many challenges have yet to be overcome before we can achieve the ultimate goal of creating human-like machines. Such a machine needs to be able to deliver satisfactory performance under a broad range of operating conditions. A much greater understanding of the human speech process is still required before automatic speaker recognition systems can approach human performance.

show abstract

“…Besacier and Bonastre found that certain sub-bands contained more speaker-specific information than others: in particular, the low-frequency region below 600 Hz and the high-frequency region above 2 kHz. This helps to explain the poorer performance rates for telephone-quality speech (e.g., Naik, Netsch, and Doddington 1989), where some of these critical high and low frequency regions are absent.…”

Section: Previous Work On Sub-band Processingmentioning

confidence: 99%

Obituary: W A (Bill) Ainsworth

2003

Computer Speech & Language

View full text Add to dashboard Cite

A growing body of recent work documents the potential benefits of sub-band processing over wideband processing in automatic speech recognition and, less usually, speaker recognition. It is often found that the subband approach delivers performance improvements (especially in the presence of noise), but not always so. This raises the question of precisely when and how sub-band processing might be advantageous, which is difficult to answer because there is as yet only a rudimentary theoretical framework guiding this work. We describe a simple sub-band speaker recognition system designed to facilitate experimentation aimed at increasing understanding of the approach. This splits the time-domain speech signal into 16 sub-bands using a bank of second-order filters spaced on the psychophysical mel scale. Each sub-band has its own separate cepstral-based recognition system, the outputs of which are combined using the sum rule to produce a final decision. We find that sub-band processing leads to worthwhile reductions in both the verification and identification error rates relative to the wideband system, decreasing the identification error rate from 3.33% to 0.56% and equal error rate for verification by approximately 50% for clean speech. The hypothesis is advanced that, unlike the wideband system, sub-band processing effectively constrains the free parameters of the speaker models to be more uniformly deployed across frequency: as such, it offers a practical solution to the bias/variance dilemma of data modeling. Much remains to be done to explore fully the new paradigm of sub-band processing. Accordingly, several avenues for future work are identified. In particular, we aim to explore the hypothesis of a practical solution to the bias/variance dilemma in more depth.

show abstract

Speaker verification over long distance telephone lines

Cited by 50 publications

References 3 publications

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

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

40 Years of Progress in Automatic Speaker Recognition

Obituary: W A (Bill) Ainsworth

Contact Info

Product

Resources

About