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Hierarchical Phoneme Classification for Improved Speech Recognition
| Content Provider | MDPI |
|---|---|
| Author | Oh, Donghoon Park, Jeong-Sik Kim, Ji-Hwan Jang, Gil-Jin |
| Copyright Year | 2021 |
| Description | Speech recognition consists of converting input sound into a sequence of phonemes, then finding text for the input using language models. Therefore, phoneme classification performance is a critical factor for the successful implementation of a speech recognition system. However, correctly distinguishing phonemes with similar characteristics is still a challenging problem even for state-of-the-art classification methods, and the classification errors are hard to be recovered in the subsequent language processing steps. This paper proposes a hierarchical phoneme clustering method to exploit more suitable recognition models to different phonemes. The phonemes of the TIMIT database are carefully analyzed using a confusion matrix from a baseline speech recognition model. Using automatic phoneme clustering results, a set of phoneme classification models optimized for the generated phoneme groups is constructed and integrated into a hierarchical phoneme classification method. According to the results of a number of phoneme classification experiments, the proposed hierarchical phoneme group models improved performance over the baseline by 3%, 2.1%, 6.0%, and 2.2% for fricative, affricate, stop, and nasal sounds, respectively. The average accuracy was 69.5% and 71.7% for the baseline and proposed hierarchical models, showing a 2.2% overall improvement. |
| Starting Page | 428 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app11010428 |
| Journal | Applied Sciences |
| Issue Number | 1 |
| Volume Number | 11 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-01-04 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Cybernetical Science Speech Recognition Phoneme Classification Clustering Recurrent Neural Networks |
| Content Type | Text |
| Resource Type | Article |
