Adjusting dysarthric speech timing using neural nets
نویسندگان
چکیده
منابع مشابه
Adjusting dysarthric speech signals to be more intelligible
This paper presents a system that transforms the speech signals of speakers with physical speech disabilities into a more intelligible orm that can be more easily understood by listeners. These transformations are based on the correction of pronunciation errors y the removal of repeated sounds, the insertion of deleted sounds, the devoicing of unvoiced phonemes, the adjustment of the empo of sp...
متن کاملExperiments in dysarthric speech recognition using artificial neural networks.
In this study, we investigated the use of artificial neural networks (ANNs) to recognize dysarthric speech. Two multilayer neural networks were developed, trained, and tested using isolated words spoken by a dysarthric speaker. One network had the fast Fourier transform (FFT) coefficients as inputs, while the other network had the formant frequencies as inputs. The effect of additional features...
متن کاملNeural timing nets
Formulations of artificial neural networks are directly related to assumptions about neural coding in the brain. Traditional connectionist networks assume channel-based rate coding, while time-delay networks convert temporally-coded inputs into rate-coded outputs. Neural timing nets that operate on time structured input spike trains to produce meaningful time-structured outputs are proposed. Ba...
متن کاملAn Automatic Dysarthric Speech Recognition Approach using Deep Neural Networks
Transcribing dysarthric speech into text is still a challenging problem for the state-of-the-art techniques or commercially available speech recognition systems. Improving the accuracy of dysarthric speech recognition, this paper adopts Deep Belief Neural Networks (DBNs) to model the distribution of dysarthric speech signal. A continuous dysarthric speech recognition system is produced, in whic...
متن کاملContinuous Speech Recognition Using Segmental Neural Nets
We present the concept of a "Segmental Neural Net" (SNN) for phonetic modeling in continuous speech recognition. The SNN takes as input all the frames of a phonetic segment and gives as output an estimate of the probability of each of the phonemes, given the input segment. By taking into account all the frames of a phonetic segment simultaneously, the SNN overcomes the wellknown conditional-ind...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Journal of the Acoustical Society of America
سال: 1991
ISSN: 0001-4966
DOI: 10.1121/1.2029579