Modeling Conversational Systems with a Spoken Dialogue Model


Modeling Conversational Systems with a Spoken Dialogue Model – Inference of conversational language from spoken utterances is a challenge in spoken dialogue, which has been discussed in numerous works. This paper investigates the task of inferring a conversational phrase from language utterances. We formulate this task as a dialogue system where the system learns a translation vector and then a natural language translation. To the best of our knowledge, this paper shows that our approach can accurately infer a conversational phrase from speech utterances, and not from language. We present an application of this approach to a conversational dialogue system by using a text-to-speech system.

The use of neural networks has made the task of speech recognition more challenging due to its inability to classify both the semantic information and the utterances of the words. In this paper, we focus on two important problems from the literature: 1. Recognition of utterances in a single speech sentence; 2. Classification of the utterances in an ensemble of speech sentences. The performance of the machine learning is evaluated using a multi-task learning (MTM) and Multi-channel (MC) learning paradigm for speech recognition. The best performance was achieved using a test set consisting of four different languages, with the goal of predicting the sentence sequence with a higher accuracy than using any other language pair.

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Modeling Conversational Systems with a Spoken Dialogue Model

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    Improved LSTM based Speech Recognition by Adaptive Deep LearningThe use of neural networks has made the task of speech recognition more challenging due to its inability to classify both the semantic information and the utterances of the words. In this paper, we focus on two important problems from the literature: 1. Recognition of utterances in a single speech sentence; 2. Classification of the utterances in an ensemble of speech sentences. The performance of the machine learning is evaluated using a multi-task learning (MTM) and Multi-channel (MC) learning paradigm for speech recognition. The best performance was achieved using a test set consisting of four different languages, with the goal of predicting the sentence sequence with a higher accuracy than using any other language pair.


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