Before revision: A lot of work has been done recently to build sound language models for the textual data , but not much such has been done in the case of speech/audio type data. In the case of text, wordscan be represented by a unique fixed-length vector. Such models for audio type data can not only lead to great advances in the speech-related natural language processing tasks but can also reduce the need for converting speech to text for performing the same.
After revision: A lot of work has been done recently to build sound language models for the textual data , but not much such has been done in the case of audio-based language models. This paper proposes a Convolutional Autoencoder based neural architecture to model syntactically and semantically adequate contextualized representations of varying length spoken words. The use of such representations can not only lead to great advances in the speech-related natural language processing tasks but can also reduce the need for converting speech to text for performing the same.