Automatic acquisition device identification from speech recordings
In this paper we present a study on the automatic identification of acquisition devices when only access to the output speech recordings is possible. A statistical characterization of the frequency response of the device contextualized by the speech content is proposed. In particular, the intrinsic...
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| creator | Garcia-Romero, D Espy-Wilson, C Y |
| description | In this paper we present a study on the automatic identification of acquisition devices when only access to the output speech recordings is possible. A statistical characterization of the frequency response of the device contextualized by the speech content is proposed. In particular, the intrinsic characteristics of the device are captured by a template, constructed by appending together the means of a Gaussian mixture trained on the device speech recordings. This study focuses on two classes of acquisition devices, namely, landline telephone handsets and microphones. Three publicly available databases are used to assess the performance of linear- and mel-scaled cepstral coefficients. A Support Vector Machine classifier was used to perform closed-set identification experiments. The results show classification accuracies higher than 90 percent among the eight telephone handsets and eight microphones tested. |
| doi_str_mv | 10.1109/ICASSP.2010.5495407 |
| format | Conference Proceeding |
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A statistical characterization of the frequency response of the device contextualized by the speech content is proposed. In particular, the intrinsic characteristics of the device are captured by a template, constructed by appending together the means of a Gaussian mixture trained on the device speech recordings. This study focuses on two classes of acquisition devices, namely, landline telephone handsets and microphones. Three publicly available databases are used to assess the performance of linear- and mel-scaled cepstral coefficients. A Support Vector Machine classifier was used to perform closed-set identification experiments. The results show classification accuracies higher than 90 percent among the eight telephone handsets and eight microphones tested.</abstract><pub>IEEE</pub><doi>10.1109/ICASSP.2010.5495407</doi><tpages>4</tpages></addata></record> |
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| ispartof | 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010, p.1806-1809 |
| issn | 1520-6149 2379-190X |
| language | eng |
| recordid | cdi_ieee_primary_5495407 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Cepstral analysis Digital speech forensics Frequency response Gaussian supervectors intrinsic fingerprint Microphones non-intrusive forensics Object recognition Speech Support vector machine classification Support vector machines Telephone sets Telephony |
| title | Automatic acquisition device identification from speech recordings |
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