Open-source model for English accent recognition - Free deployment for accurate classification of English and Irish English accents

English Speaker Accent Recognition Using Transfer Learning

Developed by keras-io

This model classifies British and Irish accents by extracting features from Yamnet.

Audio Classification #Yamnet feature extraction #Multi-dialect classification #Audio embedding analysis

Downloads 71

Release Time : 6/7/2022

Model Overview

A dense model based on Yamnet feature extraction for distinguishing English accents from the UK and Ireland. Trained on a high-quality crowdsourced speech dataset containing over 31 hours of recordings from 120 volunteers.

Model Features

Transfer learning architecture

Utilizes the pre-trained Yamnet model for audio feature extraction, significantly improving classification performance on small datasets.

High-quality dataset

Uses a dataset containing 17,877 high-quality audio files covering accents from multiple regions in the UK and Ireland.

Robust model structure

Employs 4 dense hidden layers combined with Dropout layers to effectively prevent overfitting.

Model Capabilities

English accent recognition

Audio feature extraction

Dialect classification

Use Cases

Speech analysis

Accent recognition system

Identifies regional accent characteristics of speakers

Validation accuracy 50%, AUC 0.8909

Voice assistant optimization

Optimizes voice assistant recognition based on different accents

EdTech

Language learning assistance

Helps language learners identify and imitate specific accents

🚀 tf-keras

This library provides a model for classifying UK & Ireland accents using feature extraction from Yamnet.

🚀 Quick Start

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

✨ Features

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.

📚 Documentation

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

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