### Abstract

Using the central observation that margin-based weighted classification error (modeled using Minimum Phone Error (MPE)) corresponds to the derivative with respect to the margin term of margin-based hinge loss (modeled using Maximum Mutual Information (MMI)), this article subsumes and extends margin-based MPE and MMI within a broader framework in which the objective function is an integral of MPE loss over a range of margin values. Applying the Fundamental Theorem of Calculus, this integral is easily evaluated using finite differences of MMI functionals; lattice-based training using the new criterion can then be carried out using differences of MMI gradients. Experimental results comparing the new framework with margin-based MMI, MCE and MPE on the Corpus of Spontaneous Japanese and the MIT OpenCourseWare/MIT-World corpus are presented.

Original language | English |
---|---|

Pages (from-to) | 224-227 |

Number of pages | 4 |

Journal | Unknown Journal |

Publication status | Published - 2009 |

Externally published | Yes |

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### ASJC Scopus subject areas

- Human-Computer Interaction
- Signal Processing
- Software
- Sensory Systems

### Cite this

*Unknown Journal*, 224-227.

**Margin-space integration of MPE loss via differencing of MMI functionals for generalized error-weighted discriminative training.** / McDermott, Erik; Watanabe, Shinji; Nakamura, Atsushi.

Research output: Contribution to journal › Article

*Unknown Journal*, pp. 224-227.

}

TY - JOUR

T1 - Margin-space integration of MPE loss via differencing of MMI functionals for generalized error-weighted discriminative training

AU - McDermott, Erik

AU - Watanabe, Shinji

AU - Nakamura, Atsushi

PY - 2009

Y1 - 2009

N2 - Using the central observation that margin-based weighted classification error (modeled using Minimum Phone Error (MPE)) corresponds to the derivative with respect to the margin term of margin-based hinge loss (modeled using Maximum Mutual Information (MMI)), this article subsumes and extends margin-based MPE and MMI within a broader framework in which the objective function is an integral of MPE loss over a range of margin values. Applying the Fundamental Theorem of Calculus, this integral is easily evaluated using finite differences of MMI functionals; lattice-based training using the new criterion can then be carried out using differences of MMI gradients. Experimental results comparing the new framework with margin-based MMI, MCE and MPE on the Corpus of Spontaneous Japanese and the MIT OpenCourseWare/MIT-World corpus are presented.

AB - Using the central observation that margin-based weighted classification error (modeled using Minimum Phone Error (MPE)) corresponds to the derivative with respect to the margin term of margin-based hinge loss (modeled using Maximum Mutual Information (MMI)), this article subsumes and extends margin-based MPE and MMI within a broader framework in which the objective function is an integral of MPE loss over a range of margin values. Applying the Fundamental Theorem of Calculus, this integral is easily evaluated using finite differences of MMI functionals; lattice-based training using the new criterion can then be carried out using differences of MMI gradients. Experimental results comparing the new framework with margin-based MMI, MCE and MPE on the Corpus of Spontaneous Japanese and the MIT OpenCourseWare/MIT-World corpus are presented.

UR - http://www.scopus.com/inward/record.url?scp=70450194926&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70450194926&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:70450194926

SP - 224

EP - 227

JO - Nuclear Physics A

JF - Nuclear Physics A

SN - 0375-9474

ER -