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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Karhila, R. Kurimo, M. |
| Copyright Year | 2010 |
| Description | Author affiliation: Adaptive Informatics Research Centre, Aalto University School of Science and Technology, Finland (Karhila, R.; Kurimo, M.) |
| Abstract | In this paper we present investigations on how the acoustic models in automatic speech recognition can be adapted across languages in unsupervised fashion to improve recognition of speech with a foreign accent. Recognition systems were trained on large Finnish and English corpora, and tested both on monolingual and bilingual material. Adaptation with bilingual and monolingual recognisers was compared. We found out that recognition of foreign accented English with help of Finnish adaptation training data from the same speaker was not improved significantly. However, the recognition of native Finnish using foreign accented English adaptation data was improved significantly. |
| Starting Page | 109 |
| Ending Page | 114 |
| File Size | 162752 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424479047 |
| e-ISBN | 9781424479023 |
| DOI | 10.1109/SLT.2010.5700831 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-12-12 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Error analysis multilingual acoustic modelling Adaptation model Hidden Markov models Training data Speech recognition Speech Data models automatic speech recognition cross-lingual speaker adaptation |
| Content Type | Text |
| Resource Type | Article |
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