Abstract In this paper, we focus on the problems associated with error correction of automatic speech recognition (ASR) based on confusion networks. The problems discussed are the availability of corpus in terms of calculating the semantic score and performance degradation for error correction using N -gram due to the null transitions in the confusion networks. In attempt to solve these problems, first, we employ Normalized Web Distance as a measure for semantic similarity between words that are located far from each other. The advantage of Normalized Web Distance is that it may use the Internet and so on for learning semantic similarity, which might solve the problem of corpus availability. Secondly, an error correction model without null ...
Training domain-specific automatic speech recognition (ASR) systems requires a suitable amount of da...
Dysarthria is a motor speech disorder characterized by weakness, paralysis, or poor coordination of ...
[[abstract]]We propose a novel model-based HMM distance computation framework to estimate run-time r...
International audienceThe paper proposes a new approach for a posteriori enrichment of automatic spe...
International audienceLarge vocabulary automatic speech recognition (ASR) technologies perform well ...
I experimented with Hopfield networks in the context of a voice-based, query-answering system. Hopfi...
Many application environments have already usedspeech interface. But the low speech recognition rate...
In this work we investigate new inter-phone and inter-word distances and we apply them to predict if...
In previous work, we described how learning the pattern of recognition errors made by an individual ...
Thesis (Ph.D.)--University of Washington, 2015Automatic speech recognition (ASR), the transcription ...
This thesis tackles the problem of error detection in speech recognition. First, principles of recen...
Error correction in automatic speech recognition (ASR) aims to correct those incorrect words in sent...
Current commercial dialogue systems typically use hand-crafted grammars for Spoken Language Understa...
Modern speech recognition has many ways of quantifying the misrecognitions a speech recognizer makes...
In this paper, we use a set of approaches to, efficiently, rescore the output of the automatic speec...
Training domain-specific automatic speech recognition (ASR) systems requires a suitable amount of da...
Dysarthria is a motor speech disorder characterized by weakness, paralysis, or poor coordination of ...
[[abstract]]We propose a novel model-based HMM distance computation framework to estimate run-time r...
International audienceThe paper proposes a new approach for a posteriori enrichment of automatic spe...
International audienceLarge vocabulary automatic speech recognition (ASR) technologies perform well ...
I experimented with Hopfield networks in the context of a voice-based, query-answering system. Hopfi...
Many application environments have already usedspeech interface. But the low speech recognition rate...
In this work we investigate new inter-phone and inter-word distances and we apply them to predict if...
In previous work, we described how learning the pattern of recognition errors made by an individual ...
Thesis (Ph.D.)--University of Washington, 2015Automatic speech recognition (ASR), the transcription ...
This thesis tackles the problem of error detection in speech recognition. First, principles of recen...
Error correction in automatic speech recognition (ASR) aims to correct those incorrect words in sent...
Current commercial dialogue systems typically use hand-crafted grammars for Spoken Language Understa...
Modern speech recognition has many ways of quantifying the misrecognitions a speech recognizer makes...
In this paper, we use a set of approaches to, efficiently, rescore the output of the automatic speec...
Training domain-specific automatic speech recognition (ASR) systems requires a suitable amount of da...
Dysarthria is a motor speech disorder characterized by weakness, paralysis, or poor coordination of ...
[[abstract]]We propose a novel model-based HMM distance computation framework to estimate run-time r...