TAILIEUCHUNG - Báo cáo khoa học: "Speech Recognition of Czech - Inclusion of Rare Words Helps"

Large vocabulary continuous speech recognition of inflective languages, such as Czech, Russian or Serbo-Croatian, is heavily deteriorated by excessive out of vocabulary rate. In this paper, we tackle the problem of vocabulary selection, language modeling and pruning for inflective languages. We show that by explicit reduction of out of vocabulary rate we can achieve significant improvements in recognition accuracy while almost preserving the model size. Reported results are on Czech speech corpora. . | Speech Recognition of Czech - Inclusion of Rare Words Helps PetrPodvesky and Pavel Machek Institute of Formal and Applied Linguistics Charles University Prague Czech Republic podvesky machek @ Abstract Large vocabulary continuous speech recognition of inflective languages such as Czech Russian or Serbo-Croatian is heavily deteriorated by excessive out of vocabulary rate. In this paper we tackle the problem of vocabulary selection language modeling and pruning for inflective languages. We show that by explicit reduction of out of vocabulary rate we can achieve significant improvements in recognition accuracy while almost preserving the model size. Reported results are on Czech speech corpora. 1 Introduction Large vocabulary continuous speech recognition of inflective languages is a challenging task for mainly two reasons. Rich morphology generates huge number of forms which are not captured by limited-size dictionaries and therefore leads to worse recognition results. Relatively free word order admits enormous number of word sequences and thus impoverishes n-gram language models. In this paper we are concerned with the former issue. Previous work which deals with excessive vocabulary growth goes mainly in two lines. Authors have either decided to break words into sub-word units or to adapt dictionaries in a multi-pass scenario. On Czech data Byrne et al. 2001 suggest to use linguistically motivated recognition units. Words are broken down to stems and endings and used as the recognition units in the first recognition phase. In the second phase stems and endings are concatenated. On Serbo-Croatian Geutner et al. 1998 also tested morphemes as the recognition units. Both groups of authors agreed that this approach is not beneficial for speech recognition of inflective languages. Vocabulary adaptation however brought considerable improvement. Both Icring and Psutka 2001 on Czech and Geutner et al. 1998 on Serbo-Croatian reported substantial reduction of

• Báo cáo khoa học
• Speech Recognition of Czech
• Inclusion of Rare Words Helps
• Pavel Machek
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Bài giảng Xử lý ngôn ngữ tự nhiên
• Ngôn ngữ tự nhiên
• Kỹ thuật lập trình
• Xử lý nhập nhằng ngữ nghĩa
• Xử lý ngữ nghĩa
• Xử lý ngôn ngữ
• Natural Language Processing
• Ngôn ngữ lập trình
• Nhập nhằng cấu trúc
• Tri thức về ngôn ngữ
• Mô hình n gram
• Dịch máy
• Phương pháp dịch máy
• Hiểu ngôn ngữ
• Phân tích ngữ nghĩa
• Phân loại tin tự động
• Biểu diễn vị từ
• Thuộc tính về sự kiện
• Thuộc tính về sự kiện
• Mô hình ngôn ngữ Google Book N grams
• Mô hình ngôn ngữ KenLM
• Phương pháp làm mịn
• Tách từ tiếng Việt
• Gán nhãn từ loại
• Phân tích cú pháp
• Nghĩa từ vựng
• Phân giải nhập nhằng từ
• Phân loại văn bản
• Lexical chain
• Phản hồi thông tin
• Bài toán PTCP
• Cấu trúc ngữ pháp
• Phân tích cú pháp xác suất
• CKY kết hợp xác suất
• Văn phạm phi ngữ cảnh xác suất
• Hình thái học