Name
Abstract | ||
|---|---|---|
This paper introduces a first attempt to perform phoneme-level segmentation of speech based on a perceptual representation - the Spectro Temporal Excitation Pattern (STEP) - and a dimensionality reduction technique - the t-Distributed Stochastic Neighbour Embedding (t-SNE). The method searches for the true phonetic boundaries in the vicinity of those produced by an HMM-based segmentation. It looks for perceptually-salient spectral changes which occur at these phonetic transitions, and exploits t-SNE's ability to capture both local and global structure of the data. The method is intended to be used in any language and it is therefore not tailored to any particular dataset or language. Results show that this simple approach improves segmentation accuracy of unvoiced phonemes by 4% within a 5 ms margin, and 5% at a 10 ms margin. For the voiced phonemes, however, accuracy drops slightly. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/SPED.2015.7343105 | 2015 International Conference on Speech Technology and Human-Computer Dialogue (SpeD) |
Keywords | Field | DocType |
phonetic segmentation,STEP,t-SNE,HMM acoustic model,k-Means | Scale-space segmentation,Embedding,Global structure,Dimensionality reduction,Pattern recognition,Computer science,Segmentation,Stochastic process,Speech recognition,Artificial intelligence,Hidden Markov model | Conference |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adriana Stan | 1 | 36 | 7.23 |
| Cassia Valentini-Botinhao | 2 | 208 | 18.41 |
| Mircea Giurgiu | 3 | 11 | 5.19 |
| Simon King | 4 | 19 | 5.11 |