English Accent Recognition Using Deep Machine Learning

The article highlights aspects of the use of deep machine learning to recognize the accents of the English language. The software has been developed to determine the percentage of how close audio recordings are to each of 8 most common English accents. A convolutional neural network consisting of 2 convolutional layers, 1 max pooling layer, and 2 dense layers was trained across 2 epochs on a set of 5,516 audio recordings taken from the English Multi-speaker Corpus for Voice Cloning resource. The forecasting accuracy of 89.07% was achieved on the test data presented by 11 thousand MFCC matrices with a dimension of 50×87.

Accent Recognition with Hybrid Phonetic Features

The performance of voice-controlled systems is usually influenced by accented speech. To make these systems more robust, frontend accent recognition (AR) technologies have received increased attention in recent years. As accent is a high-level abstract feature that has a profound relationship with language knowledge, AR is more challenging than other language-agnostic audio classification tasks. In this paper, we use an auxiliary automatic speech recognition (ASR) task to extract language-related phonetic features. Furthermore, we propose a hybrid structure that incorporates the embeddings of both a fixed acoustic model and a trainable acoustic model, making the language-related acoustic feature more robust. We conduct several experiments on the AESRC dataset. The results demonstrate that our approach can obtain an 8.02% relative improvement compared with the Transformer baseline, showing the merits of the proposed method.