Add to ORKGWalter O., Despotovic V., Haeb-Umbach R., Gemmeke J.F., Ons B., Van hamme H., ''An evaluation of unsupervised acoustic model training for a dysarthric speech interface'', Proceedings 15th annual conference of the International Speech Communication Association (ISCA) - Interspeech 2014, pp. 1013-1017, September 14-18, 2014, Singapore.status: publishe
Automatic speech recognition for our most widely used languages has recently seen substantial impro...
We present an experimental comparison of seven state-of-the-art machine learning algorithms for the ...
Developing automatic speech recognition (ASR) systems that recognise dysarthric speech as well as co...
In this paper, we investigate unsupervised acoustic model train-ing approaches for dysarthric-speech...
In this paper, we investigate unsupervised acoustic model training approaches for dysarthric-speech ...
Gemmeke J.F., Sehgal S., Cunningham S., Van hamme H., ''Dysarthric vocal interfaces with minimal tra...
Dysarthria is a neurological speech disorder, which exhibits multi-fold disturbances in the speech p...
Gemmeke J.F., Sehgal S., Cunningham S., ''Fast vocabulary learning for disordered speech vocal inter...
Ons B., Gemmeke J.F., Van hamme H., ''The self-taught vocal interface'', EURASIP journal on audio, s...
Speech production errors characteristic of dysarthria are chiefly responsible for the low accuracy o...
In light of steady progress in machine learning, automatic speech recognition (ASR) is entering more...
This paper presents an improved transfer learning framework applied to robust personalised speech re...
Millions of individuals have acquired or have been born with neuro-motor conditions that limit the c...
There has been much recent interest in building continuous speech recognition systems for people wi...
The need for automated speech recognition has expanded as a result of significant industrial expansi...
Automatic speech recognition for our most widely used languages has recently seen substantial impro...
We present an experimental comparison of seven state-of-the-art machine learning algorithms for the ...
Developing automatic speech recognition (ASR) systems that recognise dysarthric speech as well as co...
In this paper, we investigate unsupervised acoustic model train-ing approaches for dysarthric-speech...
In this paper, we investigate unsupervised acoustic model training approaches for dysarthric-speech ...
Gemmeke J.F., Sehgal S., Cunningham S., Van hamme H., ''Dysarthric vocal interfaces with minimal tra...
Dysarthria is a neurological speech disorder, which exhibits multi-fold disturbances in the speech p...
Gemmeke J.F., Sehgal S., Cunningham S., ''Fast vocabulary learning for disordered speech vocal inter...
Ons B., Gemmeke J.F., Van hamme H., ''The self-taught vocal interface'', EURASIP journal on audio, s...
Speech production errors characteristic of dysarthria are chiefly responsible for the low accuracy o...
In light of steady progress in machine learning, automatic speech recognition (ASR) is entering more...
This paper presents an improved transfer learning framework applied to robust personalised speech re...
Millions of individuals have acquired or have been born with neuro-motor conditions that limit the c...
There has been much recent interest in building continuous speech recognition systems for people wi...
The need for automated speech recognition has expanded as a result of significant industrial expansi...
Automatic speech recognition for our most widely used languages has recently seen substantial impro...
We present an experimental comparison of seven state-of-the-art machine learning algorithms for the ...
Developing automatic speech recognition (ASR) systems that recognise dysarthric speech as well as co...