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Abstract
We study the problem of evaluating automatic speech recognition (ASR) systems that target dialectal speech input. A major challenge in this case is that the orthography of dialects is typically not standardized. From an ASR evaluation perspective, this means that there is no clear gold standard for the expected output, and several possible outputs could be considered correct according to different human annotators, which makes standard word error rate (WER) inadequate as an evaluation metric. Such a situation is typical for machine translation (MT), and thus we borrow ideas from an MT evaluation metric, namely TERp, an extension of translation error rate which is closely-related to WER. In particular, in the process of comparing a hypothesis to a reference, we make use of spelling variants for words and phrases, which we mine from Twitter in an unsupervised fashion. Our experiments with evaluating ASR output for Egyptian Arabic, and further manual analysis, show that the resulting WERd (i.e., \emph{WER for dialects}) metric, a variant of TERp, is more adequate than WER for evaluating dialectal ASR.
Original language | English |
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Title of host publication | IEEE Automatic Speech Recognition and Understanding Workshop (ASRU 2017) |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 141-148 |
Number of pages | 8 |
ISBN (Electronic) | 978-1-5090-4788-8 |
ISBN (Print) | 978-1-5090-4789-5 |
DOIs | |
Publication status | Published - 25 Jan 2018 |
Event | 2017 IEEE Automatic Speech Recognition and Understanding Workshop - Okinawa, Japan Duration: 16 Dec 2017 → 20 Dec 2017 https://asru2017.org/ |
Conference
Conference | 2017 IEEE Automatic Speech Recognition and Understanding Workshop |
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Abbreviated title | ASRU 2017 |
Country/Territory | Japan |
City | Okinawa |
Period | 16/12/17 → 20/12/17 |
Internet address |
Fingerprint
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- 1 Finished
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SUMMA - Scalable Understanding of Mulitingual Media
Renals, S., Birch-Mayne, A. & Cohen, S.
1/02/16 → 31/01/19
Project: Research