Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Robustly detecting keywords in human speech is an important precondition for cognitive systems, which aim at intelligently interacting with users. Conventional techniques for keyword spotting usually show good performance when evaluated on ll articulated read speech. Hover, modeling natural, spontaneous, and emotionally colored speech is challenging for today’s speech recognition systems and thus requires novel approaches with enhanced robustness. In this article,  propose a new architecture for vocabulary independent keyword detection as needed for cognitive virtual agents such as the SEMAINE system. Our word spotting model is composed of a Dynamic Bayesian Network (DBN) and a bidirectional Long Short-Term Memory (BLSTM) recurrent neural net. The BLSTM network uses a self-learned amount of contextual information to provide a discrete phoneme prediction feature for the DBN, which is able to distinguish beten keywords and arbitrary speech.  evaluate our Tandem BLSTM-DBN technique on both read speech and spontaneous emotional speech and show that our method significantly outperforms conventional Hidden Markov Model-based approaches for both application scenarios.
['Martin Wöllmer', 'Florian Eyben', 'Alex Graves', 'Björn W. Schuller', 'Gerhard Rigoll']
I am writing a research paper on a idea Bidirectional LSTM Networks for Context-Sensitive Keyword Detection in a Cognitive Virtual Agent Framework
[{"from": "human", "value": "I am writing a research paper on a idea Bidirectional LSTM Networks for Context-Sensitive Keyword Detection in a Cognitive Virtual Agent Framework"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Robustly detecting keywords in human speech is an important precondition for cognitive systems, which aim at intelligently interacting with users. Conventional techniques for keyword spotting usually show good performance when evaluated on ll articulated read speech. Hover, modeling natural, spontaneous, and emotionally colored speech is challenging for today\u2019s speech recognition systems and thus requires novel approaches with enhanced robustness. In this article,  propose a new architecture for vocabulary independent keyword detection as needed for cognitive virtual agents such as the SEMAINE system. Our word spotting model is composed of a Dynamic Bayesian Network (DBN) and a bidirectional Long Short-Term Memory (BLSTM) recurrent neural net. The BLSTM network uses a self-learned amount of contextual information to provide a discrete phoneme prediction feature for the DBN, which is able to distinguish beten keywords and arbitrary speech.  evaluate our Tandem BLSTM-DBN technique on both read speech and spontaneous emotional speech and show that our method significantly outperforms conventional Hidden Markov Model-based approaches for both application scenarios."}]