Adapting a hidden Markov sound model in a speech recognition lexicon

US6460017B1 · kind B1 · utility

Assignee

• Siemens Aktiengesellschaft · DE

Inventors

• Udo Bub · Hersbruck, DE
• Harald Höge · Gauting, DE
• Joachim Köhler · Ortenburg, DE

Key dates

• Filing date: Jun 10, 1999
• Grant date: Oct 1, 2002
• Expiry date: Jun 10, 2019

Classification

• Technology area (CPC G): Physics
• CPC primary: G10L2015/0635
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

When adapting a lexicon in a speech recognition system, a code book of hidden Markov sound models made available with a speech recognition system is adapted for specific applications. These applications are thereby defined by a lexicon of the application that is modified by the user. The adaption ensues during the operation and occurs by a shift of the stored mid-point vector of the probability density distributions of hidden Markov models in the direction of a recognized feature vector of sound expressions and with reference to the specifically employed hidden Markov models. Compared to standard methods, this method has the advantage that it ensues on-line and that it assures a very high recognition rate given a low calculating outlay. Further, the outlay for training specific sound models for corresponding applications is avoided. An automatic adaption to foreign languages can ensue by applying specific hidden Markov models from multi-lingual phonemes wherein the similarities of sounds across various languages is exploited. Given the methods for the acoustically phonetic modelling thereby employed, both language-specific as well as language-independent properties are taken into c…