Speaker_partition_module

Analysis of voices based on the Mel-frequency band.
Goal: Identification of voices speaking (diarization) and calculation of speech partition (in %).

Methodology:

• Collect voice data
• Sample audio data of x speakers that talk y times to represent a round of people talking
• Annotate samples with labels and merge audio file
• Create train & test split of samples
• Train unsupervised clustering module to detect number of people
• Train supervised RNN classifier to determine who is speaking at time x

Preprocessing

• Convert files to .wav convertFlac2Wav.py
• Collect data via LibriSpeech voices library (audiofiles) audio_manipulation02.py
• Extract x random speakers with y audio samples per speaker Result: Generated audio samples of length 30-60 seconds

Feature extraction:

• Create mel-frequency spectrum for each audio file feature_extraction.py
• Define overlapping feature window for training

Training:

• Implementation of google-diarizer module
• Training accuracy is only at 40 %

Further activity

• Create own unsupervised clustering module
• Try out different libraries