A continuous speech recognition system integrating additional acoustic knowledge sources in a data-driven beam search algorithm
نویسندگان
چکیده
The paper presents a continuous speech recognition system which integrates an additional acoustic knowledge source into the data-driven beam search algorithm. Details of the object oriented implementation of the beam search algorithm will be given. Integration of additional knowledge sources is treated within the flexible framework of Dempster-Shafer theory. As a first example, a rule-based plosive detector is added to the baseline system.
منابع مشابه
Efficient Search Algorithms for Large Vocabulary Continuous Speech Recognition
Automatic speaker-independent speech recognition has made significant progress from the days of isolated word recognition. Today state of the art systems are capable of performing large-vocabulary continuous speech recognition (LVCSR) over complex domains such as news broadcasts and telephone conversations. A significant contribution to this advancement in technology is due to the development o...
متن کاملSpoken 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 کاملImproved Bayesian Training for Context-Dependent Modeling in Continuous Persian Speech Recognition
Context-dependent modeling is a widely used technique for better phone modeling in continuous speech recognition. While different types of context-dependent models have been used, triphones have been known as the most effective ones. In this paper, a Maximum a Posteriori (MAP) estimation approach has been used to estimate the parameters of the untied triphone model set used in data-driven clust...
متن کاملPersian Phone Recognition Using Acoustic Landmarks and Neural Network-based variability compensation methods
Speech recognition is a subfield of artificial intelligence that develops technologies to convert speech utterance into transcription. So far, various methods such as hidden Markov models and artificial neural networks have been used to develop speech recognition systems. In most of these systems, the speech signal frames are processed uniformly, while the information is not evenly distributed ...
متن کاملThe N-Best Algorithm: Efficient Procedure for Finding Top N Sentence Hypotheses
In this paper we introduce a new search algorithm that provides a simple, clean, and efficient interface between the speech and natural language components of a spoken language system. The N-Best algorithm is a timesynchronous Viterbi-style beam search algorithm that can be made to find the most likely N whole sentence alternatives that are within a given a "beam" of the most likely sentence. T...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 1994