Presenter : Pei- ning Chen NTNU CSIE SLP Lab

1. Error approximation and minimum phone erroracoustic model estimationMatthew Gibson and Thomas Hain Audio, Speech, and Language Processing, IEEE Transactions Presenter : Pei-ning Chen NTNU CSIE SLP Lab

2. Outline • Introduction • Minimum Phone Error Theory • Error Approximation • Limitation of Baseline Approximation Error • Alternative Error Approximations • Experiments • Error Approximation Analysis • Summary and Future Work

3. Introduction • Acoustic models estimated using the MPE technique have displayed significant classification performance improvements over ML-estimated models. • This paper introduces a novel error approximation method and demonstrates how it addresses limitations of a previously used technique, and the method is found to yield significant performance improvements when deployed for MPE acoustic model estimation.

4. MPE • The MPE criterion • : Levenshtein distance

5. Error Approximation • Alignment-based error approximation:

6. A substitution example:

7. Swap the reference and the hypothesis:

8. A insertion example:

9. A deletion example:

10. Limitations of baseline

11. Frame Error Normalisation • With deletion

12. With insertion

13. Using Multiple Reference Alignments • MSNFR and AMSNFR

14. Analysis • S : substitution, I : insertion, D : deletion

15. Reference with silence

16. Evaluation results • Unsmoothed

17. I-smoothing

18. Summary and Future work • Significant improvements over the previously introduced error approximation when the symmetrically normalised frame error approximation is deployed for MPE acoustic parameter re-estimation. • Future work should compare use of the approximate methods introduced in this paper with lattice manipulation approaches and the minimum phone frame error.