Application of continuous state Hidden Markov Models to a classical problem in speech recognition

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...

Speaker Independent Speech Recognition Using Hidden Markov Models for Persian Isolated Words

Speaker Independent Speech Recognition Using Hidden Markov Models for Persian Isolated Words

Large Vocabulary Continuous Speech Recognition

Large vocabulary speaker-independent speech recognition systems being capable of recognizing continuous speech based on hidden Markov models are today’s standard. This review introduces the fundamentals of speech and the underlying speech recognition problems. The three classical approaches, i.e., the acoustic-phonetic, the statistical (pattern) recognition and the artificial intelligence appro...

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...