Model adaptive apparatus and model adaptive method, recording medium, and pattern recognition apparatus

• US 7,043,425 B2
• Filed: 03/24/2005
• Issued: 05/09/2006
• Est. Priority Date: 12/28/1999
• Status: Expired due to Fees

• Current Assignee: Sony Corporation (Sony Group Corp.)
• Original Assignee: Sony Corporation (Sony Group Corp.)
• Inventors: Pao, Hongchang
• Primary Examiner(s): Young, W. R.
• Assistant Examiner(s): Opsasnick, Michael N.
• Application Number:
  US11/090,815
• Publication Number:
  US 20050171767A1
• Time in Patent Office: 411 Days
• Field of Search: 704/211, 704/252, 704/256
• US Class Current: 704/211
• CPC Class Codes: G10L 15/065 Adaptation
• CPC Class Codes: G10L 15/20 Speech recognition techniques specially adapted for robustness in adverse environments, e.g. in noise, of stress induced speech G10L21/02 takes precedence

Abstract

In order to improve recognition performance, a no-speech sound model correction section performs an adaptation of a no-speech sound model which is a sound model representing a no-speech state on the basis of input data observed in an interval immediately before a speech recognition interval for the object of speech recognition and the degree of freshness representing the recentness of the input data.

Claims

1. A model adaptive apparatus for performing an adaptation of a model used in pattern recognition which classifies input data in a time series into one of a predetermined number of models, said model adaptive apparatus comprising:

data extraction means for extracting said input data, corresponding to a predetermined model, which is observed in a predetermined interval, and for outputting the data as extracted data;

model adaptation means for performing an adaptation of said predetermined model on the basis of the extracted data in said predetermined interval and the degree of freshness representing the recentness of the extracted data, said freshness being a function whose value changes in a manner to correspond to the time-related position of said extracted data in said predetermined interval,

wherein said pattern recognition is performed based on a feature distribution in a feature space of said input data and a monotonically increasing function that increases as time elapses;

feature extraction means for extracting the features of said input data;

storage means for storing a predetermined number of models into which said input data is to be classified; and

classification means for classifying the features of said input data, corresponding to a predetermined model, which is observed in a predetermined interval, and for outputting the data as extracted data.