Project partner:

The main aim of the project ALSO is to expand speech recognition systems toward a speaker and paragraph-specific parametrization and automatic adaptation (of parameters), so that recognition becomes more exact; to enable more efficient implementation and to achieve greater acceptance among users.

The development of new tools for improved training is an essential part of speech recognition systems based on available data, which depict and describe the field of application exactly. This enables the user to be trained for an existing system and concurrent application, deploying user-friendly and available means. One the one hand, high initial recognition rates and improved running adaptations can be attained, whilst also assuring a broader field of applications.

Content-related aspects:

• There is an evaluation to see how individual parameters influence and specific models influence recognition performance. Which of these parameters or models bear any relevance and whether they contain sufficient potential for improvement, will be investigated. This is done manually; i.e. the models are tested against the parameters in terms of potential for improvement. Only the most promising parameters will continue to be tested.
• Selection of optimal learning procedures for these parameters
• Investigation how these parameters can be automatically detected and selected and whether a drop in performance must be taken into account.
• Integration into the current system with fully automated adjustment or appropriation for a particular user-interface. This should be done so that professional expertise is not required to adjust parameters to user-specific settings.

Keywords:

• Author-specific speech recognition parameters (hesitation parameter, auto-punctuation parameter, author-specific configurated acoustic models and variations in pronunciation)
• Paragraph-specific parameters (variations in pronunciation, speech models)
• "Unsupervised Continuous Learning"