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We describe a scheme to translate spoken English lectures into Japanese consisting of a deep neural network based English automatic speech recognition system (ASR) and an English to Japanese phrase-based statistical machine translation system (SMT). The bad influence of speech misrecognition for the translation model is focused. For coping with bad influence caused by speech misrecognition, we utilized...
One of the difficulties in sung speech recognition is the small distance in an acoustic space between phonemes in sung speech. Therefore we considered clustering the speech based on a pitch (fundamental frequency F0) and creating a larger distance between the phonemes. In addition, we considered a two-stage training method of DNN-HMM: the first stage is trained by using conventional acoustic features...
Deep neural networks (DNN) have achieved significant success in the field of speech recognition. One of the main advantages of the DNN is automatic feature extraction without human intervention. Therefore, we incorporate a pseudo-filterbank layer to the bottom of DNN and train the whole filterbank layer and the following networks jointly, while most systems take pre-defined mel-scale filterbanks as...
This paper describes our scheme to translate spoken English lectures into Japanese consisting of an English automatic speech recognition system (ASR) that utilizes a deep neural network (DNN) and an English to Japanese phrase-based statistical machine translation system (SMT). We focused on domain adaptation of the acoustic and translation models. For domain adaptation of the translation model, frequently...
In speech recognition, it is preferable not to hypothesize the details, e.g., specific age and gender, of a target user. However, speaker independence is one of the things that degrades ASR performance. In this work, we propose a speaker adaptation method to recognize a short time utterance. There have been several studies on speaker-independent DNN-HMM in which i-vector is computed, and the additional...
There is an increasing use of sensor networks capable of sensing multimedia data including audio data. Unfortunately, public use of these is not allowed because they contain crucial privacy information such as person and location names. Person name extraction (PNE), which is a widely investigated research topic, is an effective technique to resolve this problem. However, there is an important difference...
Hidden Conditional Random Fields(HCRF) is a very promising approach to model speech. However, because HCRF computes the score of a hypothesis by summing up linearly weighted features, it cannot consider non-linearity among features that will be crucial for speech recognition. In this paper, we extend HCRF by incorporating gate function used in neural networks and propose a new model called Hidden...
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