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Integrating Stress Information in Large Vocabulary Continuous Speech Recognition

机译:在大词汇量连续语音识别中整合压力信息

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In this paper we propose a novel method for integrating stress information in the decoding step of a speech recognizer. A multiscale rhythm model was used to determine the stress scores for each syllable, which are further used to reinforce paths during search. Two strategies for integrating the stress were employed: the first one reinforces paths through all the syllables with a value proportional to the their stress score, while the second one enhances paths passing only through stressed syllables, but with a constant value. The former strategy slightly outperforms the later, bringing a relative improvement of more than 2% over the baseline. Furthermore, the stress information proved to be a robust feature, by performing well even for foreign-accented speech.
机译:在本文中,我们提出了一种在语音识别器的解码步骤中整合压力信息的新颖方法。多尺度节奏模型用于确定每个音节的重音得分,并进一步用于增强搜索过程中的路径。采用了两种整合重音的策略:第一个以与音节分数成正比的值增强通过所有音节的路径,而第二个以恒定的值增强仅通过重读音节的路径。前一种策略略胜于后一种,相对于基准而言,相对提高了2%以上。此外,即使对于带有外国重音的语音也表现出色,压力信息被证明是强大的功能。

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