  In this paper we present the Amharic Speech Emotion Dataset (ASED), which
covers four dialects (Gojjam, Wollo, Shewa and Gonder) and five different
emotions (neutral, fearful, happy, sad and angry). We believe it is the first
Speech Emotion Recognition (SER) dataset for the Amharic language. 65 volunteer
participants, all native speakers, recorded 2,474 sound samples, two to four
seconds in length. Eight judges assigned emotions to the samples with high
agreement level (Fleiss kappa = 0.8). The resulting dataset is freely available
for download. Next, we developed a four-layer variant of the well-known VGG
model which we call VGGb. Three experiments were then carried out using VGGb
for SER, using ASED. First, we investigated whether Mel-spectrogram features or
Mel-frequency Cepstral coefficient (MFCC) features work best for Amharic. This
was done by training two VGGb SER models on ASED, one using Mel-spectrograms
and the other using MFCC. Four forms of training were tried, standard
cross-validation, and three variants based on sentences, dialects and speaker
groups. Thus, a sentence used for training would not be used for testing, and
the same for a dialect and speaker group. The conclusion was that MFCC features
are superior under all four training schemes. MFCC was therefore adopted for
Experiment 2, where VGGb and three other existing models were compared on ASED:
RESNet50, Alex-Net and LSTM. VGGb was found to have very good accuracy (90.73%)
as well as the fastest training time. In Experiment 3, the performance of VGGb
was compared when trained on two existing SER datasets, RAVDESS (English) and
EMO-DB (German) as well as on ASED (Amharic). Results are comparable across
these languages, with ASED being the highest. This suggests that VGGb can be
successfully applied to other languages. We hope that ASED will encourage
researchers to experiment with other models for Amharic SER.
