On the Impact of Voice Encoding and Transmission on the Predictions of Speaker Warmth and Attractiveness

Modern human-computer interaction systems may not only be based on interpreting natural language but also on detecting speaker interpersonal characteristics in order to determine dialog strategies. This may be of high interest in diferent felds such as telephone marketing or automatic voice-based interactive services. However, when such systems encounter signals transmitted over a communication network instead of clean speech, e.g., in call centers, the speaker characterization accuracy might be impaired by the degradations caused in the speech signal by the encoding and communication processes.
This article addresses a binary classifcation of high versus low warm–attractive speakers over diferent channel and encoding conditions. The ground truth is derived from ratings given to clean speech extracted from an extensive subjective test. Our results show that, under the considered conditions, the AMR-WB+ codec permits good levels of classifcation accuracy, comparable to the classifcation with clean, non-degraded speech. This is especially notable for the case of a Random Forest-based classifer, which presents the best performance among the set of evaluated algorithms. The impact of diferent packet loss rates has been examined, whereas jitter efects have been found to be negligible.