Contextualized Spoken Word Representations from Convolutional Autoencoders
This addresses the gap in audio-based NLP tasks for applications like speech processing, though it appears incremental as it adapts existing autoencoder methods to spoken data.
The paper tackles the problem of building audio-based language models for spoken words, proposing a Convolutional Autoencoder architecture to generate contextualized representations, and demonstrates robustness by evaluating on three benchmark datasets for word similarities against text-based models.
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.