Publications

Refine Results

(Filters Applied) Clear All

By

Fine structure features for speaker identification

May 7, 1996
  |
Conference Paper
Author:
Charles R. Jankowski Jr
…
Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, Vol. 2, Speech (Part II), 7-10 May 1996, pp. 689-692.
Topic:
speaker recognition
R&D area:
R&D group:

Summary

The performance of speaker identification (SID) systems can be improved by the addition of the rapidly varying "fine structure" features of formant amplitude and/or frequency modulation and multiple excitation pulses. This paper shows how the estimation of such fine structure features can be improved further by obtaining better estimates of formant frequency locations and uncovering various sources of error in the feature extraction systems. Most female telephone speech showed "spurious" formants, due to distortion in the telephone network. Nevertheless, SID performance was greatest with these spurious formants as formant estimates. A new feature has also been identified which can increase SID performance: cepstral coefficients from noise in the estimated excitation waveform. Finally, statistical tools have been developed to explore the relative importance of features used for SID, with the ultimate goal of uncovering the source of the features that provide SID performance improvement.
READ LESS

Summary

The performance of speaker identification (SID) systems can be improved by the addition of the rapidly varying "fine structure" features of formant amplitude and/or frequency modulation and multiple excitation pulses. This paper shows how the estimation of such fine structure features can be improved further by obtaining better estimates of...

READ MORE
Fine structure features for speaker identification

A comparison of signal processing front ends for automatic word recognition

July 1, 1995
  |
Journal Article
Author:
Charles R. Jankowski Jr
…
Published in:
IEEE Trans. Speech Audio Process., Vol. 3, No. 4, July 1995, pp. 286-293.
Topic:
speech recognition
R&D area:
R&D group:

Summary

This paper compares the word error rate of a speech recognizer using several signal processing front ends based on auditory properties. Front ends were compared with a control mel filter banks (MFB) based cepstral front end in clean speech and with speech degraded by noise and spectral variability, using the TI-105 isolated word database. MFB recognition error rates ranged from 0.5 to 3.1%,, and the reduction in error rates provided by auditory models was less than 0.5 percentage points. Some earlier studies that demonstrated considerably more improvement with auditory models used linear predictive coding (LPC) based control front ends. This paper shows that MFB cepstra significantly outperform LPC cepstra under noisy conditions. Techniques using an optimal linear combination of features for data reduction were also evaluated.
READ LESS

Summary

This paper compares the word error rate of a speech recognizer using several signal processing front ends based on auditory properties. Front ends were compared with a control mel filter banks (MFB) based cepstral front end in clean speech and with speech degraded by noise and spectral variability, using the...

READ MORE
A comparison of signal processing front ends for automatic word recognition

Measuring fine structure in speech: application to speaker identification

May 9, 1995
  |
Conference Paper
Author:
Charles R. Jankowski Jr
…
Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, Vol. 1, 9-12 May 1995, pp. 325-328.
Topic:
speaker recognition
R&D area:
R&D group:

Summary

The performance of systems for speaker identification (SID) can be quite good with clean speech, though much lower with degraded speech. Thus it is useful to search for new features for SID, particularly features that are robust over a degraded channel. This paper investigates features that are based on amplitude and frequency modulations of speech formants, high resolution measurement of fundamental frequency and location of "secondary pulses," measured using a high-resolution energy operator. When these features are added to traditional features using an existing SID system with a 168 speaker telephone speech database, SID performance improved by as much as 4% for male speakers and 8.2% for female speakers.
READ LESS

Summary

The performance of systems for speaker identification (SID) can be quite good with clean speech, though much lower with degraded speech. Thus it is useful to search for new features for SID, particularly features that are robust over a degraded channel. This paper investigates features that are based on amplitude...

READ MORE
Measuring fine structure in speech: application to speaker identification

Energy onset times for speaker identification

November 1, 1994
  |
Journal Article
Author:
Thomas F. Quatieri
…
Published in:
IEEE Signal Process. Lett., Vol. 1, No. 11, November 1994, pp. 160-162.
Topic:
speaker recognition
R&D area:
R&D group:

Summary

Onset times of resonant energy pulses are measured with the high-resolution Teager operator and used as features in the Reynolds Gaussian-mixture speaker identification algorithm. Feature sets are constructed with primary pitch and secondary pulse locations derived from low and high speech formants. Preliminary testing was performed with a confusable 40-speaker subset from the NTIMIT (telephone channel) database. Speaker identification improved from 55 to 70% correct classification when the full set of new resonant energy-based features were added as an independent stream to conventional mel-cepstra.
READ LESS

Summary

Onset times of resonant energy pulses are measured with the high-resolution Teager operator and used as features in the Reynolds Gaussian-mixture speaker identification algorithm. Feature sets are constructed with primary pitch and secondary pulse locations derived from low and high speech formants. Preliminary testing was performed with a confusable 40-speaker...

READ MORE
Energy onset times for speaker identification

Formant AM-FM for speaker identification

October 25, 1994
  |
Conference Paper
Author:
Charles R. Jankowski Jr
…
Published in:
Proc. IEEE-SP Int. Symp. on Time-Frequency and Time-Scale Analysis, 25-28 October 1994, pp. 608-611.
Topic:
speaker recognition
R&D area:
R&D group:

Summary

The performance of systems for speaker identification (SID) can be quite good with clean speech, though much lower with degraded speech. Thus it is useful to search for new features for SID, particularly features that are robust over a degraded channel. This paper investigates features that are robust over a degraded channel. This paper investigates features that are based on amplitude and frequency modulations of speech formants. Such modulations are measured using a high-resolution energy operator and related algorithms for recovering amplitude and frequency from an AM-FM signal. When these features are added to traditional features using an existing SID system with a telephone speech database, SID performance improved by as much as 15%. Energy onset time measurements that yielded improved SID performance are also discussed.
READ LESS

Summary

The performance of systems for speaker identification (SID) can be quite good with clean speech, though much lower with degraded speech. Thus it is useful to search for new features for SID, particularly features that are robust over a degraded channel. This paper investigates features that are robust over a...

READ MORE
Formant AM-FM for speaker identification

Wordspotter training using figure-of-merit back propagation

April 19, 1994
  |
Conference Paper
Author:
Richard P. Lippmann
…
Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, Vol. 1, Speech Processing, 19-22 April 1994, pp. 389-392.
Topic:
machine learning
R&D area:
R&D group:

Summary

A new approach to wordspotter training is presented which directly maximizes the Figure of Merit (FOM) defined as the average detection rate over a specified range of false alarm rates. This systematic approach to discriminant training for wordspotters eliminates the necessity of ad hoc thresholds and tuning. It improves the FOM of wordspotters tested using cross-validation on the credit-card speech corpus training conversations by 4 to 5 percentage points to roughly 70% This improved performance requires little extra complexity during wordspotting and only two extra passes through the training data during training. The FOM gradient is computed analytically for each putative hit, back-propagated through HMM word models using the Viterbi alignment, and used to adjust RBF hidden node centers and state-weights associated with every node in HMM keyword models.
READ LESS

Summary

A new approach to wordspotter training is presented which directly maximizes the Figure of Merit (FOM) defined as the average detection rate over a specified range of false alarm rates. This systematic approach to discriminant training for wordspotters eliminates the necessity of ad hoc thresholds and tuning. It improves the...

READ MORE
Wordspotter training using figure-of-merit back propagation
1-6 of 6