DETEKSI OOV MENGGUNAKAN HASIL PENGENALAN SUARA OTOMATIS UNTUK BAHASA INDONESIA

Paper ini menjelaskan tentang implementasi pengenalan OOV (Out of Vocabulary) words pada Aplikasi Pengenal Suara Berbahasa Indonesia. Pengenalan OOV words penting karena masalah ini tidak dapat diselesaikan dengan menambah ukuran kamus. Untuk mengimplementasi pengenalan OOV words, dilakukan transduksi fonem ke kata. Klasifikasi kata-kata diberikan dengan melihat model bahasa dan probabilitas perubahan fonem untuk menentukan bagian yang termasuk OOV words. Pada paper ini juga dilakukan evaluasi terhadap beberapa jenis kamus yang digunakan pada sistem pengenal suara. Modifikasi pada kamus sistem pengenal bahasa Indonesia menghasilkan peningkatan sekitar 4% sedangkan hasil deteksi akurasi OOV sebesar sekitar 77%

Empirical properties of multilingual phone-to-word transduction,” in

This paper explores the error-robustness of phone-to-word transduction across a variety of languages. We implement a noisy channel model in which a phonetic input stream is corrupted by an error model, and then transduced back to words using the inverse error model and linguistic constraints. By controlling the error level, we are able to measure the sensitivity of different languages to degradation in the phonetic input stream. This analysis is carried further to measure the importance of each phone in each language individually. We study Arabic, Chinese, English, German and Spanish, and find that they behave similarly in this paradigm: in each case, a phone error produces about 1.4 word errors, and frequently incorrect phones matter slightly less than others. In the absence of phone errors, transduced word errors are still present, and we use the conditional entropy of words given phones to explain the observed behavior. Index Terms — Speech recognition, phonetic decoding, transduction, multilingual, AS