Task-specific, personalized Automatic Speech Recognition

Voice User Interfaces (VUI) are particularly useful if the operator has to work hands-free or if her cognitive load is very high.This is the case, e.g., when the operator can be easily disturbed by the environment, the operational task induces stress and there is little or no fault tolerance.However, the factors that contribute to the usefulness of a VUI also complicate the design.Automatic Speech Recognition (ASR) must be robust in noisy environments, under non-optimal microphone conditions and for different types of speech -including stress-induced shouting, hyper articulation and heavy breathing, among others.Commercially available, generic ASR solutions do not fulfil high robustness requirements under these conditions.However, ASR systems can be made robust if they are tailored to their respective use cases and personalised for specific users.This paper introduces a method to customize a Large Vocabulary Continuous Speech Recognizer (LVCSR) system to achieve such robustness.An LVCSR system includes a language model (LM) and an acoustic model (AM).The customization involves adapting both the LM and the AM to the specific operational context.For LM customization, we employ an Use Case Editor (UCE) that provides an intuitive interface, enabling users to align linguistic models with their unique needs.For AM customization, a Multi Speaker Text to Speech Synthesis (MSTTS) module is used to automatically generate personalized speech data, ensuring the model captures the distinctive characteristics of individual speakers.Together, these adaptations ensure the LVCSR system is configured to meet the demands of challenging environments and diverse users.