Gender classification in two Emotional Speech databases

By M. Kotti, Constantine Kotropoulos. 2008

56 citations recorded by the source corpus.

Gender classification is a challenging problem, which finds applications in speaker indexing, speaker recognition, speaker diarization, annotation and retrieval of multimedia databases, voice synthesis, smart human-computer interaction, biometrics, social robots etc. Although it has been studied for more than thirty years, by no means it is a solved problem. Processing emotional speech in order to identify speakerpsilas gender makes the problem even more interesting. A large pool of 1379 features is created including 605 novel features. A branch and bound feature selection algorithm is applied to select a subset of 15 features among the 1379 originally extracted. Support vector machines with various kernels are tested as gender classifiers, when applied to two databases, namely: the Berlin database of Emotional Speech and the Danish Emotional Speech database. The reported classification results out perform those obtained by state-of-the-art techniques, since a perfect classification accuracy is obtained.

Subfields: Artificial Intelligence, Signal Processing

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Citations in the corpus

Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion? (2019, background): “…speech processing algorithms can infer not only the spoken contents from the speech signal, but also the speaker’s identity [5], intention [6–9], gender [10,11], emotional state [12–14], pathological condition [15–17], personality [18,19] and cultural [20,21] attributes to a great extent.”