Recovery from Model Inconsistency in Multilingual Speech Recognition

Current ASR has difficulties in handling unexpected words that are typically replaced by acoustically acceptable high prior probability words. Identifying parts of the message where such a replacement could have happened may allow for corrective strategies.

We aim to develop data-guided techniques that would yield unconstrained estimates of posterior probabilities of sub-word classes employed in the stochastic model solely from the acoustic evidence, i.e. without use of higher level language constraints.

These posterior probabilities then could be compared with the constrained estimates of posterior probabilities derived with the constraints implied by the underlying stochastic model. Parts of the message where any significant mismatch between these two probability distributions is found should be re-examined and corrective strategies applied.

This may allow for development of systems that are able to indicate when they “do not know” and eventually may be able to “learn-as-you-go” in applications encountering new situations and new languages.

During the 2007 Summer Workshop we intend to focus on detection and description of out-of-vocabulary and mispronounced words in the 6 language Call-home database. Additionally, in order to describe the suspect parts of the message, we will work on language-independent recognizer of speech sounds that could be applied for phonetic transcription of identified suspect parts of the recognized message.

Closing Presentations
Final Report

Team Members
Senior Members
Sanjeev Khudanpur CLSP
Hynek Hermansky CLSP
Lukas Burget Brno University of Technology
Chin-Hui Lee Georgia Technical Institute
Haizhong Li Institute for Infocomm Research
Jon Nedel Department of Defense
Geoffrey Zweig Microsoft
Graduate Students
Ariya Rastrow CLSP
Pavel Matejka Brno University of Technology
Petr Schwartz Brno University of Technology
Rong Tong Nanyang Technological University
Chris White CLSP
Undergraduate Students
Mirko Hannemann Magdeburg University, Germany
Sally Isaacoff University of Michigan
Puneet Sahani NSIT; Delhi University

Center for Language and Speech Processing