Mild Cognitive Impairment (MCI) is a heterogeneous clinical syndrome, often considered as the prodromal stage of dementia. It is characterized by the subtle deterioration of cognitive functions, including memory, executive functions and language. Mainly due to the tenuous nature of these impairments, a high percentage of MCI cases remain undetected. There is evidence that language changes in MCI are present even before the manifestation of other distinctive cognitive symptoms, which offers a chance for early recognition. A cheap non-invasive way of early screening could be the use of automatic speech analysis. Earlier, our research team developed a set of speech temporal parameters, and demonstrated its applicability for MCI detection. For the automatic extraction of these attributes, a Hungarian-language ASR system was employed to match the native language of the MCI and healthy control (HC) subjects. In practical applications, however, it would be convenient to use exactly the same tool, regardless of the language spoken by the subjects. In this study we show that our temporal parameter set, consisting of articulation rate, speech tempo and various other attributes describing the hesitation of the subject, can indeed be reliably extracted regardless of the language of the ASR system used. For this purpose, we performed experiments both on English-speaking and on Hungarian-speaking MCI patients and healthy control subjects, using English and Hungarian ASR systems in both cases. Our experimental results indicate that the language on which the ASR system was trained only slightly affects the MCI classification performance, because we got quite similar scores (67-92%) as we did in the monolingual cases (67-92% as well). As our last investigation, we compared the proposed attribute values for the same utterances, utilizing both the English and the Hungarian ASR models. We found that the articulation rate and speech tempo values calculated based on the two ASR models were highly correlated, and so were the attributes corresponding to silent pauses; however, noticeable differences were found regarding the filled pauses (still, these attributes remained indicative for both languages). Our further analysis revealed that this is probably due to a difference regarding the annotation of the English and the Hungarian ASR training utterances.

轻度认知障碍（MCI）是一种异质性临床综合征，通常被视为痴呆症的前驱阶段。它的特征是认知功能（包括记忆，执行功能和语言）的细微下降。主要是由于这些损害的脆弱性，仍有很大比例的MCI病例未被发现。有证据表明，即使在其他独特的认知症状出现之前，MCI中的语言也会出现变化，这为早期识别提供了机会。一种便宜的，非侵入性的早期筛查方法可以是使用自动语音分析。早些时候，我们的研究团队开发了一组语音时间参数，并证明了其在MCI检测中的适用性。为了自动提取这些属性，使用匈牙利语言ASR系统来匹配MCI和健康对照（HC）受试者的母语。但是，在实际应用中，无论对象使用哪种语言，都可以使用完全相同的工具。在这项研究中，我们表明，无论使用哪种ASR系统的语言，确实可以可靠地提取我们的时间参数集，包括发音速度，语速和描述对象犹豫的各种其他属性。为此，我们在两种情况下都使用英语和匈牙利ASR系统对讲英语和讲匈牙利语的MCI患者和健康对照者进行了实验。我们的实验结果表明，训练ASR系统所用的语言对MCI分类性能的影响很小，因为我们获得的评分与单语案例中的得分（67-92％）非常相似（67-92％）。作为我们的最后调查，我们使用英语和匈牙利ASR模型比较了相同话语的建议属性值。我们发现，基于两个ASR模型计算的发音速度和语音速度值高度相关，与沉默暂停相对应的属性也高度相关。但是，在填充的停顿方面发现了明显的差异（不过，这些属性仍然对两种语言都具有指示性）。我们的进一步分析表明，这可能是由于英语和匈牙利ASR训练话语的注释存在差异。我们使用英语和匈牙利ASR模型比较了相同话语的建议属性值。我们发现，基于两个ASR模型计算的发音速度和语音速度值高度相关，与沉默暂停相对应的属性也高度相关。但是，在填充的停顿方面发现了明显的差异（不过，这些属性仍然对两种语言都具有指示性）。我们的进一步分析表明，这可能是由于在英语和匈牙利ASR训练话语的注释方面存在差异。我们使用英语和匈牙利ASR模型比较了相同话语的建议属性值。我们发现，基于两个ASR模型计算的发音速度和语音速度值高度相关，与沉默暂停相对应的属性也高度相关。但是，在填充的停顿方面发现了明显的差异（不过，这些属性仍然对两种语言都具有指示性）。我们的进一步分析表明，这可能是由于在英语和匈牙利ASR训练话语的注释方面存在差异。沉默的停顿所对应的属性也是如此；但是，在填充的停顿方面发现了明显的差异（不过，这些属性仍然对两种语言都具有指示性）。我们的进一步分析表明，这可能是由于在英语和匈牙利ASR训练话语的注释方面存在差异。沉默的停顿所对应的属性也是如此；但是，在填充的停顿方面发现了明显的差异（不过，这些属性仍然对两种语言都具有指示性）。我们的进一步分析表明，这可能是由于在英语和匈牙利ASR训练话语的注释方面存在差异。