Comparing K-means and Optics clustering algorithms for identifying vowel categories
Bibliographic Data
| ID | 22159641 |
|---|---|
| Authors | Emily Grabowski (University of California, Berkeley), Jennifer Kuo (0000-0001-5078-7882, University of California, Los Angeles) |
| Year | 2023 |
| Volume | 8 |
| Issue | 1 |
| Pages | 5488 |
| Publication date | 2023-04-27 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Proceedings of the Linguistic Society of America (JOURNAL) |
| Journal identifiers | ISSN: 2473-8689 • E-ISSN: 2473-8689 |
| Publisher | Linguistic Society of America (PUBLISHER • US) |
| DOI | 10.3765/plsa.v8i1.5488 |
| OpenAlex | W4367173089 |
| Language | EN |
| References cited | 7 |
The K-means algorithm is the most commonly used clustering method for phonetic vowel description but has some properties that may be sub-optimal for representing phonetic data. This study compares K-means with an alternative algorithm, OPTICS, in two speech styles (lab vs. conversational) in English to test whether OPTICS is a viable alternative to K-means for characterizing vowel spaces. We find that with noisier data, OPTICS identifies clusters that more accurately represent the underlying data. Our results highlight the importance of choosing an algorithm whose assumptions are in line with the phonetic data being considered
Algorithm · Cluster analysis · k-means clustering · Speech recognition · Vowel · Computer Science · Music and Audio Processing · Speech and Audio Processing · Speech Recognition and Synthesis · Artificial Intelligence
DBSCAN Revisited, Revisited
Acoustic characteristics of American English vowels
Silhouettes
Automatic phonetic classification of vocalic allophones in Tol
Who Belongs in the Family
Variation in the lexical distribution and implementation of phonetically similar phonemes in Catalan
Numerical Simulation of Vowel Quality Systems
| Citation velocity | historical |
|---|---|
| Highly cited | No |