This project, supporting experimental research methods for large speech recognition tasks, aims at purchasing a large Symmetric Multi-Processor (SMP) system. The research involves the development of models and algorithms that will reduce automatic speech recognition (ASR) errors for natural conversations, which may be exacerbated by realistic but difficult acoustic conditions. Major improvement in algorithm robustness opens a wider range of future applications, including voice access to networked information and information retrieval and extraction for meetings. Head-mounted microphones or microphone arrays may not be feasible due to low Signal to Noise Ratio (SNR) and the effects of reverberation. Integration of multiple estimators, either at the level of probability streams or hypothesized word sequences, with associated confidence measures, can greatly improve overall performance. Research has shown that such properties can significantly increase recognition accuracy, even for high SNR tasks that require the transcription of informal conversational speech. For problems of scale, training of even a single-stream system can take weeks using a 2005-generation PC or workstation. A fast multi-processor system might overcome these resource limitations and greatly enhance the ability to explore promising solutions to the current constraints on performance. Hence, research requiring multiple probability streams or more computationally intensive algorithms should benefit from this new multi-layered system infrastructure.
Broader Impact: The planned research supports technical explorations that become the basis of PhD dissertations. Other areas, such as computational biology, natural language processing, digital communications, computer vision, human activity modeling, and human computer interaction, might also benefit by the research. ICSI involves many female researchers; has a high school outreach program, and trains students, and other investigators.
