The application of the linear prediction Model for
speech waveform analysis to context-independent automatic
speaker recognition is explored, primarily in terns of the
parametric sensitivity of the model. Feature vectors to
characterize speakers are formed from linear prediction
speech parameters computed as inverse filter coefficients,
reflection coefficients or cepstral coefficients, and also
power spectrum parameters via Fast Fourier Transform coefficients.
The comparative performance of these parameters is
investigated in speaker recognition experiments. The stability
of the linear prediction parameters is tested over a
range of model order from p=6 to p=30. Two independent
speech databases are used to substantiate the experimental
results.
The quality of the automatic recognition technique is
assessed in a novel experiment based on a direct performance
comparison with the human skill of aural recognition.
Correlation is sought between the performance of the aural
and automatic recognition methods, for each of the four parameter
sets. Although the recognition accuracy of the automatic system is superior to that of the direct aural technique,
the error distributions are highly variable. The performance
of the automatic system is shown to be empirically
based and unlike the intuitive human process.
An extended preamble to the description of the experiments
reviews the current art of automatic speaker recognition,
with a critical consideration of the performance of
linear prediction techniques. As supported by our experimental
results, it is concluded that success in the laboratory
rests upon a rather fragile foundation. Application to
problems beyond the controlled laboratory environment is
seen, therefore, to be still more precarious.
| Identifer | oai:union.ndltd.org:ADTP/218846 |
| Date | January 1982 |
| Creators | Collins, Anthony McLaren, n/a |
| Publisher | University of Canberra. Information Sciences |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | ), Copyright Anthony McLaren Collins |
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