tailieunhanh - Báo cáo khoa học: "Applying a Grammar-based Language Model to a Simplified Broadcast-News Transcription Task"

We propose a language model based on a precise, linguistically motivated grammar (a hand-crafted Head-driven Phrase Structure Grammar) and a statistical model estimating the probability of a parse tree. The language model is applied by means of an N-best rescoring step, which allows to directly measure the performance gains relative to the baseline system without rescoring. To demonstrate that our approach is feasible and beneficial for non-trivial broad-domain speech recognition tasks, we applied it to a simplified German broadcast-news transcription task. We report a significant reduction in word error rate compared to a state-of-the-art baseline system. . | Applying a Grammar-based Language Model to a Simplified Broadcast-News Transcription Task Tobias Kaufmann Speech Processing Group ETH Zurich Zurich Switzerland kaufmann@ Beat Pfister Speech Processing Group ETH Zurich Zurich Switzerland pfister@ Abstract We propose a language model based on a precise linguistically motivated grammar a hand-crafted Head-driven Phrase Structure Grammar and a statistical model estimating the probability of a parse tree. The language model is applied by means of an N-best rescoring step which allows to directly measure the performance gains relative to the baseline system without rescoring. To demonstrate that our approach is feasible and beneficial for non-trivial broad-domain speech recognition tasks we applied it to a simplified German broadcast-news transcription task. We report a significant reduction in word error rate compared to a state-of-the-art baseline system. 1 Introduction It has repeatedly been pointed out that N-grams model natural language only superficially an Nth-order Markov chain is a very crude model of the complex dependencies between words in an utterance. More accurate statistical models of natural language have mainly been developed in the field of statistical parsing . Collins 2003 Charniak 2000 and Ratnaparkhi 1999 . Other linguistically inspired language models like Chelba and Jelinek 2000 and Roark 2001 have been applied to continuous speech recognition. These models have in common that they explicitly or implicitly use a context-free grammar induced from a treebank with the exception of Chelba and Jelinek 2000 . The probability of a rule expansion or parser operation is conditioned on various contextual information and the derivation history. An important reason for the success of these models is the fact that they are lexicalized the probability distributions are also conditioned on the actual words occuring in the utterance and not only on their parts of speech. Most .

• Báo cáo khoa học
• Applying a Grammar based Language Model
• Simplified Broadcast News Transcription Task
• Tobias Kaufmann
• báo cáo khoa học
• báo cáo 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 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