Sentiment analysis in non-fixed length audios using a Fully Convolutional Neural Network
This enables near real-time sentiment analysis for applications like call centers and medical consultations, though it appears incremental as it adapts existing techniques to handle non-fixed audio lengths.
The authors tackled sentiment analysis in variable-length audio by proposing a Fully Convolutional Neural Network that uses Mel spectrogram and MFCC features, achieving results that outperform state-of-the-art methods on EMODB, RAVDESS, and TESS datasets.
In this work, a sentiment analysis method that is capable of accepting audio of any length, without being fixed a priori, is proposed. Mel spectrogram and Mel Frequency Cepstral Coefficients are used as audio description methods and a Fully Convolutional Neural Network architecture is proposed as a classifier. The results have been validated using three well known datasets: EMODB, RAVDESS, and TESS. The results obtained were promising, outperforming the state-of-the-art methods. Also, thanks to the fact that the proposed method admits audios of any size, it allows a sentiment analysis to be made in near real time, which is very interesting for a wide range of fields such as call centers, medical consultations, or financial brokers.