Assessment of prosody is important for diagnosis and remediation of speech and language disorders, for diagnosis of neurological conditions, and for foreign language instruction. Current assessment is largely auditory-perceptual, which has obvious drawbacks; however, automation of assessment faces numerous obstacles. We propose methods for automatically assessing production of lexical stress, focus, phrasing, pragmatic style, and vocal affect. Speech was analyzed from children in six tasks designed to elicit specific prosodic contrasts. The methods involve dynamic and global features, using spectral, fundamental frequency, and temporal information. The automatically computed scores were validated against mean scores from judges who, in all but one task, listened to “prosodic minimal pairs” of recordings, each pair containing two utterances from the same child with approximately the same phonemic material but differing on a specific prosodic dimension, such as stress. The judges identified the prosodic categories of the two utterances and rated the strength of their contrast. For almost all tasks, we found that the automated scores correlated with the mean scores approximately as well as the judges’ individual scores. Real-time scores assigned during examination – as is fairly typical in speech assessment – correlated substantially less than the automated scores with the mean scores.

韵律评估对于言语和语言障碍的诊断和矫正、神经系统疾病的诊断以及外语教学都很重要。目前的评估主要是听觉感知，具有明显的缺陷；然而，评估自动化面临许多障碍。我们提出了自动评估词汇重音、焦点、措辞、语用风格和声音影响的产生的方法。在旨在引发特定韵律对比的六项任务中对儿童的言语进行了分析。这些方法涉及使用频谱、基频和时间信息的动态和全局特征。自动计算的分数根据评委的平均分数进行验证，评委除了一项任务外，在所有任务中都听取了“韵律最小对”录音，每对包含来自同一个孩子的两个话语，具有大致相同的音素材料，但在特定的韵律维度上有所不同，例如重音。评委们确定了两种话语的韵律类别，并对它们的对比强度进行了评级。对于几乎所有任务，我们发现自动评分与平均评分以及评委的个人评分大致相关。在考试期间分配的实时分数——这在语音评估中是相当典型的——与平均分数的自动分数相比要小得多。我们发现自动评分与平均评分以及评委的个人评分大致相关。在考试期间分配的实时分数——这在语音评估中是相当典型的——与平均分数的自动分数相比要小得多。我们发现自动评分与平均评分以及评委的个人评分大致相关。在考试期间分配的实时分数——这在语音评估中是相当典型的——与平均分数的自动分数相比要小得多。