Browsing by Author "Singh S"

Now showing 1 - 3 of 3
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    Integrated environmental process planning for the design and manufacture of automotive components
    (Taylor & Francis, 2007) Singh S; Goodyer JE; Popplewell K
    Advanced product quality planning (APQP) logic is widely used by manufacturers for the design and manufacture of automotive components. Manufacturers are increasingly finding difficulties to incorporate environmental considerations in the broad range of products that they manufacture. Therefore, there is a need for a systematic method for environmental process planning to evaluate product configurations and their associated environmental impact. The framework and models discussed in this paper can deal with a variety of product characteristics and environmental impacts through a selection of environmental performance indicators (EPIs) for a final product configuration. The framework and models have been applied in a real-life application and have proven that changes in product design or process selection can reduce the product's environmental impact and increase process efficiency. Hence, manufacturers can use the framework and models during the APQP process to benchmark each product variation that they manufacture in a standardized manner and realize cost saving opportunities.
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    Real and synthetic Punjabi speech datasets for automatic speech recognition
    (Elsevier Inc, 2024-02) Singh S; Hou F; Wang R
    Automatic speech recognition (ASR) has been an active area of research. Training with large annotated datasets is the key to the development of robust ASR systems. However, most available datasets are focused on high-resource languages like English, leaving a significant gap for low-resource languages. Among these languages is Punjabi, despite its large number of speakers, Punjabi lacks high-quality annotated datasets for accurate speech recognition. To address this gap, we introduce three labeled Punjabi speech datasets: Punjabi Speech (real speech dataset) and Google-synth/CMU-synth (synthesized speech datasets). The Punjabi Speech dataset consists of read speech recordings captured in various environments, including both studio and open settings. In addition, the Google-synth dataset is synthesized using Google's Punjabi text-to-speech cloud services. Furthermore, the CMU-synth dataset is created using the Clustergen model available in the Festival speech synthesis system developed by CMU. These datasets aim to facilitate the development of accurate Punjabi speech recognition systems, bridging the resource gap for this important language.