presenter : pei- ning chen ntnu csie slp lab

Error approximation and minimum phone erroracoustic model estimation

Matthew Gibson and Thomas Hain

Presenter : Pei-ning ChenNTNU CSIE SLP Lab

Audio, Speech, and Language Processing, IEEE Transactions

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

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.

MPE

• The MPE criterion

• : Levenshtein distance

R

r

rMNr

N

WwMPE wwLowp

RR

N1111 ˆ,,|1

1

rMN wwL 11 ˆ,

Error Approximation

• Alignment-based error approximation:

different z and q if,1

label same z and q if,21max

zqezqe

qAz

label reference goverlappin : z

q with overlaps which z of proportion theis, zqe

• A substitution example:

• Swap the reference and the hypothesis:

• A insertion example:

• A deletion example:

Limitations of baseline

Frame Error Normalisation

• With deletion

• With insertion

Using Multiple Reference Alignments

• MSNFR and AMSNFR

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

• Reference with silence

Evaluation results• Unsmoothed

I-smoothing

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.