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首页> 外文期刊>EURASIP journal on embedded systems >Speech Silicon: An FPGA Architecture for Real-Time Hidden Markov-Model-Based Speech Recognition
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Speech Silicon: An FPGA Architecture for Real-Time Hidden Markov-Model-Based Speech Recognition

机译：语音芯片：基于实时隐马尔可夫模型的语音识别的FPGA架构

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This paper examines the design of an FPGA-based system-on-a-chip capable of performing continuous speech recognition on medium sized vocabularies in real time. Through the creation of three dedicated pipelines, one for each of the major operations in the system, we were able to maximize the throughput of the system while simultaneously minimizing the number of pipeline stalls in the system. Further, by implementing a token-passing scheme between the later stages of the system, the complexity of the control was greatly reduced and the amount of active data present in the system at any time was minimized. Additionally, through in-depth analysis of the SPHINX 3 large vocabulary continuous speech recognition engine, we were able to design models that could be efficiently benchmarked against a known software platform. These results, combined with the ability to reprogram the system for different recognition tasks, serve to create a system capable of performing real-time speech recognition in a vast array of environments.
机译：本文研究了一种基于FPGA的片上系统的设计，该系统能够对中型词汇实时执行连续语音识别。通过创建三个专用管道，每个专用管道用于系统中的每个主要操作，我们能够最大化系统的吞吐量，同时最小化系统中的管道停顿数量。此外，通过在系统的后续阶段之间实施令牌传递方案，可以大大降低控制的复杂性，并使系统中随时存在的活动数据量最小化。此外，通过对SPHINX 3大词汇量连续语音识别引擎的深入分析，我们能够设计可以有效地参照已知软件平台进行基准测试的模型。这些结果与为不同的识别任务对系统进行重新编程的能力相结合，有助于创建一个能够在各种环境中执行实时语音识别的系统。

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