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. This paper proposes a novel model architecture that produces syntactically, contextualized, and semantically adequate representation of varying length spoken words. The performance of the spoken word embeddings generated by the proposed model was validated by (1) inspecting the vector space generated , and (2) evaluating its performance on the downstream task of next spoken word prediction in a speech .
A lot of work has been done to build text-based language models for performing different NLP tasks , but not much research 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 audio-based NLP tasks but can also curtail the loss of information like tone, expression, accent, etc while converting speech to text to perform these tasks. The performance of the proposed model is validated by (1) examining the generated vector space , and (2) evaluating its performance on three benchmark datasets for measuring word similarities, against existing widely used text-based language models that are trained on the transcriptions. The proposed model was able to demonstrate its robustness when compared to the other two language-based models .
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speech/audio type data. In the case of text, wordscan be represented by a unique fixed-length vector. Such models for audio type data
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
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speech-related natural language processing
audio-based NLP
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reduce the need for
curtail the loss of information like tone, expression, accent, etc while
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for performing the same. This paper proposes a novel model architecture that produces syntactically, contextualized, and semantically adequate representation of varying length spoken words.
to perform these tasks.
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spoken word embeddings generated by the proposed model was
proposed model is
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inspecting the vector space generated
examining the generated vector space
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the downstream task of next spoken word prediction in a speech
three benchmark datasets for measuring word similarities, against existing widely used text-based language models that are trained on the transcriptions. The proposed model was able to demonstrate its robustness when compared to the other two language-based models