TAILIEUCHUNG - Báo cáo khoa học: "Dialect Classification for online podcasts fusing Acoustic and Language based Structural and Semantic Information"

The variation in speech due to dialect is a factor which significantly impacts speech system performance. In this study, we investigate effective methods of combining acoustic and language information to take advantage of (i) speaker based acoustic traits as well as (ii) content based word selection across the text sequence. For acoustics, a GMM based system is employed and for text based dialect classification, we proposed n-gram language models combined with Latent Semantic Analysis (LSA) based dialect classifiers. . | Dialect Classification for online podcasts fusing Acoustic and Language based Structural and Semantic Information Rahul Chitturi John. . Hansen1 Center for Robust Speech Systems CRSS Erik Jonsson School of Engineering and Computer Science University of Texas at Dallas Richardson Texas 75080 Abstract The variation in speech due to dialect is a factor which significantly impacts speech system performance. In this study we investigate effective methods of combining acoustic and language information to take advantage of i speaker based acoustic traits as well as ii content based word selection across the text sequence. For acoustics a GMM based system is employed and for text based dialect classification we proposed n-gram language models combined with Latent Semantic Analysis LSA based dialect classifiers. The performance of the individual classifiers is established for the three dialect family case DC rates vary from . The final combined system achieved a DC accuracy of and significantly outperforms the baseline acoustic classifier with a relative improvement of 30 confirming that an integrated dialect classification system is effective for American British and Australian dialects. 1 Introduction Automatic Dialect Classification has recently gained substantial interest in the speech processing community Gray and Hansen 2005 Hansen et al. 2004 NIST LRE 2005 . Dialect classification systems have been employed to improve the performance for Automatic Speech Recognition ASR by employing dialect dependent acoustic and language models Di-akoloukas et al. 1997 and for Rich Indexing of Spoken Document Retrieval Systems Gray and Hansen 2005 . Huang and Hansen 2005 2006 focused on identifying pronunciation differences for dialect classification. In this study unsupervised MFCC based GMM classifiers are employed for pronunciation modeling. However English dialects differ in many ways other than pronunciation .

• Báo cáo khoa học
• Dialect Classification for online podcasts fusing Acoustic
• Language based Structural
• Semantic Information
• 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