Supra-segmental Feature Based Speaker Trait Detection
نویسندگان
چکیده
It is well known that speech utterances convey a rich diversity of information concerning the speaker in addition to related semantic content. Such information may contain speaker traits such as personality, likability, health/pathology, etc. To detect speaker traits in human computer interface is an important task toward formulating more efficient and natural computer engagement. This study proposes two groups of supra-segmental features for improving speaker trait detection performance. Compared with the 6125 dimension features based baseline system, the proposed supra-segmental system not only improves performance by 9.0%, but also is computationally attractive and proper for real life application since it derives a less than 63 dimension features, which are 99% less than the baseline system.
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