Leveraging MFCC and Mel-Spectrogram Representations for Deep Learning-Based Speech Recognition
Abstract
This work propose an audio feature pipeline to support machine learning tasks through the extraction of the Mel Frequency Cepstral Coefficients and Mel-spectrogram which is then used as the input of an Convolutional Neural Network which is trained to make the classification tasks. This approach enables de creation of a rich-feature dataset and an end-to-end pipeline that reduces the gap between the audio and Machine Learning ready models with application in sound classification, speech recognition and spatial audio analysis.
Fields of Study & Subjects
Speech Recognition and Synthesis
Music and Audio Processing
Speech and Audio Processing
Publication details
- Journal / source
- Summer School on Artificial Intelligence in Cybersecurity
- Pages
- 22–22
- Publication date
- 5 February 2026
- Language
- EN