Combining phonetic attributes using conditional random fields
نویسندگان
چکیده
A Conditional Random Field is a mathematical model for sequences that is similar in many ways to a Hidden Markov Model, but is discriminative rather than generative in nature. Here we explore the application of the CRF model to ASR processing by building a system that performs first-pass phonetic recogintion using discriminatively trained phonetic attributes. This system achieves an accuracy level in a phone recognition task superior to that of an HMM that has been comparably trained.
منابع مشابه
Combining phonetic attributes usin
A Conditional Random Field is a mathematical model for sequences that is similar in many ways to a Hidden Markov Model, but is discriminative rather than generative in nature. In this paper, we explore the application of the CRF model to ASR processing of discriminative phonetic features by building a system that performs first-pass phonetic recognition using discriminatively trained phonetic f...
متن کاملDetection-based ASR in the automatic speech attribute transcription project
We present methods of detector design in the Automatic Speech Attribute Transcription project. This paper details the results of a student-led, cross-site collaboration between Georgia Institute of Technology, The Ohio State University and Rutgers University. The work reported in this paper describes and evaluates the detection-based ASR paradigm and discusses phonetic attribute classes, method...
متن کاملDiscriminative Phonetic Recognition with Conditional Random Fields
A Conditional Random Field is a mathematical model for sequences that is similar in many ways to a Hidden Markov Model, but is discriminative rather than generative in nature. In this paper, we explore the application of the CRF model to ASR processing of discriminative phonetic features by building a system that performs first-pass phonetic recognition using discriminatively trained phonetic f...
متن کاملAttribute-based Mandarin speech recognition using conditional random fields
Integrating phonetic knowledge into a speech recognizer is a possible way to further improve the performance of conventional HMM-based speech recognition methods. This paper presents a cascaded architecture which consists of attribute detection and conditional random field to make use of phonetic knowledge within the phone decoding process. The attribute detection can be implemented by using an...
متن کاملDeep-structured hidden conditional random fields for phonetic recognition
We extend our earlier work on deep-structured conditional random field (DCRF) and develop deep-structured hidden conditional random field (DHCRF). We investigate the use of this new sequential deep-learning model for phonetic recognition. DHCRF is a hierarchical model in which the final layer is a hidden conditional random field (HCRF) and the intermediate layers are zero-th-order conditional r...
متن کامل