Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Direct word discovery from audio speech signals is a very difficult and challenging problem for a developmental robot. Human infants are able to discover words directly from speech signals, and, to understand human infants’ developmental capability using a constructive approach, it is very important to build a machine learning system that can acquire knowledge about words and phonemes, i.e. a language model and an acoustic model, autonomously in an unsupervised manner. To achieve this, the nonparametric Bayesian double articulation analyzer (NPB-DAA) with the deep sparse autoencoder (DSAE) is proposed in this paper. The NPB-DAA has been proposed to achieve totally unsupervised direct word discovery from speech signals. Hover, the performance was still unsatisfactory, although it outperformed pre-existing unsupervised learning methods. ,  integrate the NPB-DAA with the DSAE, which is a neural network model that can be trained in an unsupervised manner, and demonstrate its performance throu...
['Tadahiro Taniguchi', 'Ryo Nakashima', 'Hailong Liu', 'Shogo Nagasaka']
I am writing a research paper on a idea Double articulation analyzer with deep sparse autoencoder for unsupervised word discovery from speech signals
[{"from": "human", "value": "I am writing a research paper on a idea Double articulation analyzer with deep sparse autoencoder for unsupervised word discovery from speech signals"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Direct word discovery from audio speech signals is a very difficult and challenging problem for a developmental robot. Human infants are able to discover words directly from speech signals, and, to understand human infants\u2019 developmental capability using a constructive approach, it is very important to build a machine learning system that can acquire knowledge about words and phonemes, i.e. a language model and an acoustic model, autonomously in an unsupervised manner. To achieve this, the nonparametric Bayesian double articulation analyzer (NPB-DAA) with the deep sparse autoencoder (DSAE) is proposed in this paper. The NPB-DAA has been proposed to achieve totally unsupervised direct word discovery from speech signals. Hover, the performance was still unsatisfactory, although it outperformed pre-existing unsupervised learning methods. ,  integrate the NPB-DAA with the DSAE, which is a neural network model that can be trained in an unsupervised manner, and demonstrate its performance throu..."}]