Foreign English Accent Adjustment by Learning Phonetic Patterns
This addresses the challenge of limited data for rare accents in ASR systems, though it is incremental as it builds on existing phonological methods.
The paper tackles the problem of automatic speech recognition for rare accents by proposing a model that automatically learns phonetic patterns from small datasets and generates accented word variations, achieving 59% accuracy in recognizing accented words.
State-of-the-art automatic speech recognition (ASR) systems struggle with the lack of data for rare accents. For sufficiently large datasets, neural engines tend to outshine statistical models in most natural language processing problems. However, a speech accent remains a challenge for both approaches. Phonologists manually create general rules describing a speaker's accent, but their results remain underutilized. In this paper, we propose a model that automatically retrieves phonological generalizations from a small dataset. This method leverages the difference in pronunciation between a particular dialect and General American English (GAE) and creates new accented samples of words. The proposed model is able to learn all generalizations that previously were manually obtained by phonologists. We use this statistical method to generate a million phonological variations of words from the CMU Pronouncing Dictionary and train a sequence-to-sequence RNN to recognize accented words with 59% accuracy.