Compact and robust speech recognition for embedded use on microprocessors

N. Hataoka, H. Kokubo, Y. Obuchi, A. Amano
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引用次数: 10

Abstract

We propose a compact and noise robust embedded speech recognition system implemented on microprocessors aiming for sophisticated HMIs (human machine interfaces) of car information systems. The compactness is essential for embedded systems because there are strict restrictions of CPU (central processing unit) power and available memory capacities. In this paper, first we report noise robust acoustic HMMs (hidden Markov models) and a compact spectral subtraction (SS) method after exhausting evaluation stages using real speech data recorded at car running environments. Next, we propose very novel memory assignment of acoustic models based on the product codes or sub-vector quantization technique resulting on 1 fourth memory reduction for the 2000-word vocabulary.
紧凑和强大的语音识别用于嵌入式微处理器
针对汽车信息系统中复杂的人机界面,我们提出了一种基于微处理器的紧凑且噪声鲁棒的嵌入式语音识别系统。紧凑性对于嵌入式系统至关重要,因为对CPU(中央处理单元)功率和可用内存容量有严格的限制。在本文中,我们首先报告了噪声鲁棒声学hmm(隐马尔可夫模型)和紧凑的频谱减法(SS)方法,该方法使用了在汽车运行环境中记录的真实语音数据,在耗尽评估阶段后。接下来,我们提出了一种基于产品编码或子向量量化技术的声学模型记忆分配方法,使2000字词汇的记忆减少了1 / 4。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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