Comments (4)
speech per chosen unit of time
from 3d-convolutional-speaker-recognition.
@MSAlghamdi , thank you!
In the code is this the number of wav files for each speaker?
from 3d-convolutional-speaker-recognition.
@Chegde8 , after some signal processing steps on the wav file, it takes it and divides it in chunks of utterances. As I understood from the paper (if im not mistaken), each utterance is 800 ms sound sample. Each 10 ms (one overlapped frame) of the utterance has 40 MFEC features (now we have 80 frames x40 features). As the paper states "Each input feature map has the dimensionality of ζ × 80 × 40 which is formed from 80 input frames and their corresponding spectral features, where ζ is the number of utterances used in modeling the speaker during the development and enrollment stages. By default we set ζ = 20." ζ is the depth of the 3D.
from 3d-convolutional-speaker-recognition.
Got it. Thank you!
from 3d-convolutional-speaker-recognition.
Related Issues (20)
- Convolution expects input with rank 4, got 5 HOT 9
- Extracting VAD for our own dataset HOT 1
- How to generate data HOT 13
- Run time error in the demo HOT 6
- No such file or directory: 'results/SCORES/score_vector.npy HOT 1
- Where does input_feature.py store it's results? HOT 1
- How to make Speaker Verification (1:1 recognition) model in keras? HOT 1
- ValueError: axes don't match array
- What does low-level and high-level features extraction mean? HOT 1
- .wav inputs specifics HOT 6
- where is score_vector.npy HOT 2
- Demo video recording link is broken
- Please update the vedio link of the demo.
- Pre trained model HOT 1
- Does anyone know the EER of this repo? HOT 1
- How to deal with .hdfs5 files ? HOT 2
- version problem HOT 1
- Speakers for Enrollment and Development
- All Dependency of This Project HOT 1
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from 3d-convolutional-speaker-recognition.