Automatic speaker profiling from short duration speech data

• Published in: Speech communication 2020-08, Vol.121, p.16-28
• Main Authors: Kalluri, Shareef Babu, Vijayasenan, Deepu, Ganapathy, Sriram
• Format: Article
• Language: English
• Subjects: Age; Chronology; Construction; Datasets; Formants; Harmonics; Parameter estimation; Physical properties; Regression analysis; Regression models; Short duration; Speaker profiling; Speech; Speech duration; Speech recognition; Streams; Support vector machines; Voice recognition
• ISSN: 0167-6393; 1872-7182
• DOI: 10.1016/j.specom.2020.03.008

•Speaker profiling scenario using the short duration and multilingual setting.•A common set of features for age and other physical parameters’ (height, weight, shoulder size, waist size) estimation.•Harmonic frequency location and amplitude features are proposed for physical parameter estimation.•Duration analysis is performed to determine the minimal duration of speech required to estimate each physical parameter. Many paralinguistic applications of speech demand the extraction of information about the speaker characteristics from as little speech data as possible. In this work, we explore the estimation of multiple physical parameters of the speaker from the short duration of speech in a multilingual setting. We explore different feature streams for age and body build estimation derived from the speech spectrum at different resolutions, namely – short-term log-mel spectrogram, formant features and harmonic features of the speech. The statistics of these features over the speech recording are used to learn a support vector regression model for speaker age and body build estimation. The experiments performed on the TIMIT dataset show that each of the individual features is able to achieve results that outperform previously published results in height and age estimation. Furthermore, the estimation errors from these different feature streams are complementary, which allows the combination of estimates from these feature streams to further improve the results. The combined system from short audio snippets achieves a performance of 5.2 cm, and 4.8 cm in Mean Absolute Error (MAE) for male and female respectively for height estimation. Similarly in age estimation the MAE is of 5.2 years, and 5.6 years for male, and female speakers respectively. We also extend the same physical parameter estimation to other body build parameters like shoulder width, waist size and weight along with height on a dataset we collected for speaker profiling. The duration analysis of the proposed scheme shows that the state of the art results can be achieved using only around 1–2 s of speech data. To the best of our knowledge, this is the first attempt to use a common set of features for estimating the different physical traits of a speaker.