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english-speaker-accent-recognition-using-transfer-learning

Audio Classification
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library_name: tf-keras
tags:
  - audio-classification
  - accent-classification

Model description

This model classifies UK & Ireland accents using feature extraction from Yamnet.

Yamnet Model

Yamnet is an audio event classifier trained on the AudioSet dataset to predict audio events from the AudioSet ontology. It is available on TensorFlow Hub. Yamnet accepts a 1-D tensor of audio samples with a sample rate of 16 kHz.
As output, the model returns a 3-tuple:

  • Scores of shape (N, 521) representing the scores of the 521 classes.
  • Embeddings of shape (N, 1024).
  • The log-mel spectrogram of the entire audio frame.

We will use the embeddings, which are the features extracted from the audio samples, as the input to our dense model.

For more detailed information about Yamnet, please refer to its TensorFlow Hub page.

Dense Model

The dense model that we used consists of:

  • An input layer which is embedding output of the Yamnet classifier.
  • 4 dense hidden layers and 4 dropout layers.
  • An output dense layer.

Training and evaluation data

The dataset used is the Crowdsourced high-quality UK and Ireland English Dialect speech data set which consists of a total of 17,877 high-quality audio wav files.

This dataset includes over 31 hours of recording from 120 vounteers who self-identify as native speakers of Southern England, Midlands, Northern England, Wales, Scotland and Ireland.

For more info, please refer to the above link or to the following paper: Open-source Multi-speaker Corpora of the English Accents in the British Isles

Training procedure

Training hyperparameters

Optimizer learning_rate decay beta_1 beta_2 epsilon amsgrad training_precision
Adam 1.9644e-05 0.0 0.9 0.999 1e-07 False float32

Training Metrics

Epochs Training Loss Training Accuracy Training AUC
1 10.614 0.343 0.759
2 9.378 0.396 0.806
3 8.993 0.422 0.821
4 8.768 0.433 0.829
5 8.636 0.438 0.833
6 8.514 0.442 0.837
7 8.432 0.444 0.839
8 8.339 0.446 0.841
9 8.270 0.448 0.843
10 8.202 0.449 0.845
11 8.141 0.451 0.847
12 8.095 0.452 0.849
13 8.029 0.454 0.851
14 7.982 0.454 0.852
15 7.935 0.456 0.853
16 7.896 0.456 0.854
17 7.846 0.459 0.856
18 7.809 0.460 0.857
19 7.763 0.460 0.858
20 7.720 0.462 0.860
21 7.688 0.463 0.860
22 7.640 0.464 0.861
23 7.593 0.467 0.863
24 7.579 0.467 0.863
25 7.552 0.468 0.864
26 7.512 0.468 0.865
27 7.477 0.469 0.866
28 7.434 0.470 0.867
29 7.420 0.471 0.868
30 7.374 0.471 0.868
31 7.352 0.473 0.869
32 7.323 0.474 0.870
33 7.274 0.475 0.871
34 7.253 0.476 0.871
35 7.221 0.477 0.872
36 7.179 0.480 0.873
37 7.155 0.481 0.874
38 7.141 0.481 0.874
39 7.108 0.482 0.875
40 7.067 0.483 0.876
41 7.060 0.483 0.876
42 7.019 0.485 0.877
43 6.998 0.484 0.877
44 6.974 0.486 0.878
45 6.947 0.487 0.878
46 6.921 0.488 0.879
47 6.875 0.490 0.880
48 6.860 0.490 0.880
49 6.843 0.491 0.881
50 6.811 0.492 0.881
51 6.783 0.494 0.882
52 6.764 0.494 0.882
53 6.719 0.497 0.883
54 6.693 0.497 0.884
55 6.682 0.498 0.884
56 6.653 0.497 0.884
57 6.630 0.499 0.885
58 6.596 0.500 0.885
59 6.577 0.500 0.886
60 6.546 0.501 0.886
61 6.517 0.502 0.887
62 6.514 0.504 0.887
63 6.483 0.504 0.888
64 6.428 0.506 0.888
65 6.424 0.507 0.889
66 6.412 0.508 0.889
67 6.388 0.507 0.889
68 6.342 0.509 0.890
69 6.309 0.510 0.891
70 6.300 0.510 0.891
71 6.279 0.512 0.892
72 6.258 0.510 0.892
73 6.242 0.513 0.892
74 6.206 0.514 0.893
75 6.189 0.516 0.893
76 6.164 0.517 0.894
77 6.134 0.517 0.894
78 6.120 0.517 0.894
79 6.081 0.520 0.895
80 6.090 0.518 0.895
81 6.052 0.521 0.896
82 6.028 0.521 0.896
83 5.991 0.521 0.897
84 5.974 0.524 0.897
85 5.964 0.524 0.897
86 5.951 0.524 0.897
87 5.940 0.524 0.898
88 5.891 0.525 0.899
89 5.870 0.526 0.899
90 5.856 0.528 0.899
91 5.831 0.528 0.900
92 5.808 0.529 0.900
93 5.796 0.529 0.900
94 5.770 0.530 0.901
95 5.763 0.529 0.901
96 5.749 0.530 0.901
97 5.742 0.530 0.901
98 5.705 0.531 0.902
99 5.694 0.533 0.902
100 5.671 0.534 0.903

Model Plot

View Model Plot

Model Image

Validation Results

The model achieved the following results on the validation dataset:

Results Validation
Accuracy 50%
AUC 0.8909
d-prime 1.742

And the confusion matrix for the validation set is: Validation Confusion Matrix

Credits

Author: Fadi Badine.
Based on the following Keras example English speaker accent recognition using Transfer Learning by Fadi Badine
Check out the demo space here