Speech Recognition using Artificial Neural Networks and Hidden Markov Models
نویسندگان
چکیده
In this paper, we compare two different methods for automatic Arabic speech recognition for isolated words and sentences. Isolated word/sentence recognition was performed using cepstral feature extraction by linear predictive coding, as well as Hidden Markov Models (HMM) for pattern training and classification. We implemented a new pattern classification method, where we used Neural Networks trained using the Al-Alaoui Algorithm. This new method gave comparable results to the already implemented HMM method for the recognition of words, and it has overcome HMM in the recognition of sentences. The speech recognition system implemented is part of the Teaching and Learning Using Information Technology (TLIT) project which would implement a set of reading lessons to assist adult illiterates in developing better reading capabilities.
منابع مشابه
Improving Phoneme Sequence Recognition using Phoneme Duration Information in DNN-HSMM
Improving phoneme recognition has attracted the attention of many researchers due to its applications in various fields of speech processing. Recent research achievements show that using deep neural network (DNN) in speech recognition systems significantly improves the performance of these systems. There are two phases in DNN-based phoneme recognition systems including training and testing. Mos...
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 کاملSpeaker Independent Speech Recognition Using Hidden Markov Models for Persian Isolated Words
متن کامل
Speaker Independent Speech Recognition Using Hidden Markov Models for Persian Isolated Words
متن کامل
شبکه عصبی پیچشی با پنجرههای قابل تطبیق برای بازشناسی گفتار
Although, speech recognition systems are widely used and their accuracies are continuously increased, there is a considerable performance gap between their accuracies and human recognition ability. This is partially due to high speaker variations in speech signal. Deep neural networks are among the best tools for acoustic modeling. Recently, using hybrid deep neural network and hidden Markov mo...
متن کاملHybrid HMM/Neural Network based Speech Recognition in Loquendo ASR
This paper describes hybrid Hidden Markov Models / Artificial Neural Networks (HMM/ANN) models devoted to speech recognition, and in particular Loquendo HMM/ANN, that is the core of Loquendo ASR. While Hidden Markov Models (HMM) is a dominant approach in most state-of-the-art speaker-independent, continuous speech recognition systems (and commercial products), Artificial Neural Networks (ANN) a...
متن کامل