Name
Title | ||
|---|---|---|
Human And Automated Scoring Of Fluency, Pronunciation And Intonation During Human-Machine Spoken Dialog Interactions |
Abstract | ||
|---|---|---|
We present a spoken dialog-based framework for the computer-assisted language learning (CALL) of conversational English. In particular, we leveraged the open-source HALEF dialog framework to develop a job interview conversational application. We then used crowdsourcing to collect multiple interactions with the system from non-native English speakers. We analyzed human-rated scores of the recorded dialog data on three different scoring dimensions critical to the delivery of conversational English - fluency, pronunciation and intonation/stress - and further examined the efficacy of automatically-extracted, hand-curated speech features in predicting each of these sub-scores. Machine learning experiments showed that trained scoring models generally perform at par with the human inter-rater agreement baseline in predicting human-rated scores of conversational proficiency. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.21437/Interspeech.2017-1213 | 18TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2017), VOLS 1-6: SITUATED INTERACTION |
Keywords | Field | DocType |
dialog systems, computer assisted language learning, conversational assessment, intelligent tutoring systems, crowdsourcing | Pronunciation,Human–machine system,Spoken dialog,Computer science,Fluency,Speech recognition | Conference |
ISSN | Citations | PageRank |
2308-457X | 0 | 0.34 |
References | Authors | |
7 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vikram Ramanarayanan | 1 | 70 | 13.97 |
| Patrick Lange | 2 | 9 | 8.42 |
| Keelan Evanini | 3 | 1 | 4.42 |
| Hillary R. Molloy | 4 | 0 | 0.34 |
| David Suendermann-Oeft | 5 | 9 | 2.63 |