Phoneme recognition using time-delay neural networks
نویسندگان
چکیده
In this paper we present a Time-Delay Neural Network (TDNN) approach to phoneme recognition which is characterized by two important properties. 1) Using a 3 layer arrangement of simple computing units, a hierarchy can be constructed that allows for the formation of arbitrary nonlinear decision surfaces. The TDNN learns these decision surfaces automatically using error backpropagation 111. 2) The time-delay arrangement enables the network to discover acoustic-phonetic features and the temporal relationships between them independent of position in time and hence not blurred by temporal shifts
منابع مشابه
Continuous Speech Phoneme Recognition Using Dynamic Artificial Neural Networks
Phoneme classification and recognition is the first step to large vocabulary continuous speech recognition. This step represents the acoustic modeling part of such a system. In hybrid speech recognition systems phoneme recognition is made by artificial neural networks (ANN’s). The main objective of this paper is the investigation of dynamic ANN’s, namely the Time-Delay Neural Networks (TDNN) an...
متن کاملNew variant of the Self Organizing Map in Pulsed Neural Networks to Improve Phoneme Recognition in Continuous Speech
Speech recognition has gradually improved over the years, phoneme recognition in particular. Phoneme recognition plays very important role in speech processing. Phoneme strings are basic representation for automatic language recognition and it is proved that language recognition results are highly correlated with phoneme recognition results. Nowadays, many recognizers are based on Artificial ne...
متن کاملSpotting Japanese CV-Syllables and Phonemes Using Time-Delay Neural Networks
Syllable or phoneme spotting if reliably achieved, provides a good solution to the spoken word andlor continuous speech recognition problem, . We previously showed tha t the Time-Delay Neural Network (TDNN) provided excellent recognition performance (98.6%) of the "BDG" consonant task. We would also like to extend the encouraging performance of TDNN to wordlcontinuous speech recognition. In thi...
متن کاملPhoneme recognition using time-warping neural networks
This paper proposes a novel neural network architecture for phoneme-based speech recognition. The new architecture is composed of five time-warping sub-networks and an output layer which integrates the sub-networks. Each time-warping sub-network has a different time-warping function embedded between the input layer and the first hidden layer. A time-warping sub-network recognizes the input spee...
متن کاملImproving 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 کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- IEEE Trans. Acoustics, Speech, and Signal Processing
دوره 37 شماره
صفحات -
تاریخ انتشار 1989