Towards distributed recognition of emotion from speech

This paper introduces an approach for performing distributed speech emotion recognition in a client-server architecture. In this architecture, the client side deals only with feature extraction, compression and bit-stream formatting, while the server side performs bit-stream decoding, feature decompression and emotion recognition, which requires more computational resources. Taking into account the trade-off between the required transmission bandwidth and recognition accuracy, we propose to employ a vector quantization approach based on independent codebooks for feature sub-spaces. Extensive test runs are conducted to reveal the impact of quantization parameters on the compression rate and recognition performance. In the result, by using a quantization strategy involving 32 subvectors and 9 bit codeword length, almost 30 times compression can be reached without a considerable increase of the error rate.