The RWTH large vocabulary continuous speech recognition system
نویسندگان
چکیده
In this paper, we present an overview of the RWTH Aachen large vocabulary continuous speech recognizer. The recognizer is based on continuous density hidden Markov models and a time-synchronous left-to-right beam search strategy. Experimental results on the ARPA Wall Street Journal (WSJ) corpus verify the effects of several system components, namely linear discriminant analysis, vocal tract normalization, pronunciation lexicon and cross-word triphones, on the recognition performance.
منابع مشابه
Spoken Term Detection for Persian News of Islamic Republic of Iran Broadcasting
Islamic Republic of Iran Broadcasting (IRIB) as one of the biggest broadcasting organizations, produces thousands of hours of media content daily. Accordingly, the IRIBchr('39')s archive is one of the richest archives in Iran containing a huge amount of multimedia data. Monitoring this massive volume of data, and brows and retrieval of this archive is one of the key issues for this broadcasting...
متن کاملThe Rwth Speech Recognition System and Spoken Document Retrieval
In this paper, we present an overview of the RWTH Aachen large vocabulary continuous speech recognizer. The recognizer is based on continuous density hidden Markov models and a time-synchronous left-to-right beam search strategy. Experimental results on the ARPA Wall Street Journal (WSJ) corpus verify the effects of several system components, namely linear discriminant analysis, vocal tract nor...
متن کاملThe RWTH Aachen German and English LVCSR systems for IWSLT-2013
In this paper, German and English large vocabulary continuous speech recognition (LVCSR) systems developed by the RWTH Aachen University for the IWSLT-2013 evaluation campaign are presented. Good improvements are obtained with state-of-the-art monolingual and multilingual bottleneck features. In addition, an open vocabulary approach using morphemic sub-lexical units is investigated along with t...
متن کاملFast likelihood computation methods for continuous mixture densities in large vocabulary speech recognition
This paper studies algorithms for reducing the computational e ort of the mixture density calculations in HMM-based speech recognition systems. These likelihood calculations take about 70 85% of the total recognition time in the RWTH system for large vocabulary continuous speech recognition. To reduce the computational cost of the likelihood calculations, we investigate several space partitioni...
متن کاملSpeech Input Acoustic Analysis Phoneme Inventory Pronunciation Lexicon Language Model
This paper gives an overview of an architecture and search organization for large vocabulary, continuous speech recognition (LVCSR at RWTH). In the rst part of the paper, we describe the principle and architecture of a LVCSR system. In particular, the issues of modeling and search for phoneme based recognition are discussed. In the second part, we review the word conditioned lexical tree search...
متن کامل