Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recent work has begun exploring neural acoustic word embeddings--fixed-dimensional vector representations of arbitrary-length speech segments corresponding to words. Such embeddings are applicable to speech retrieval and recognition tasks, where reasoning about whole words may make it possible to avoid ambiguous sub-word representations. The main idea is to map acoustic sequences to fixed-dimensional vectors such that examples of the same word are mapped to similar vectors, while different-word examples are mapped to very different vectors. In this work  take a multi-view approach to learning acoustic word embeddings, in which  jointly learn to embed acoustic sequences and their corresponding character sequences.  use deep bidirectional LSTM embedding models and multi-view contrastive losses.  study the effect of different loss variants, including fixed-margin and cost-sensitive losses. Our acoustic word embeddings improve over previous approaches for the task of word discrimination.  also present results on other tasks that are enabled by the multi-view approach, including cross-view word discrimination and word similarity.
['Wanjia He', 'Weiran Wang', 'Karen Livescu']
I am writing a research paper on a idea Multi-view Recurrent Neural Acoustic Word Embeddings
[{"from": "human", "value": "I am writing a research paper on a idea Multi-view Recurrent Neural Acoustic Word Embeddings"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recent work has begun exploring neural acoustic word embeddings--fixed-dimensional vector representations of arbitrary-length speech segments corresponding to words. Such embeddings are applicable to speech retrieval and recognition tasks, where reasoning about whole words may make it possible to avoid ambiguous sub-word representations. The main idea is to map acoustic sequences to fixed-dimensional vectors such that examples of the same word are mapped to similar vectors, while different-word examples are mapped to very different vectors. In this work  take a multi-view approach to learning acoustic word embeddings, in which  jointly learn to embed acoustic sequences and their corresponding character sequences.  use deep bidirectional LSTM embedding models and multi-view contrastive losses.  study the effect of different loss variants, including fixed-margin and cost-sensitive losses. Our acoustic word embeddings improve over previous approaches for the task of word discrimination.  also present results on other tasks that are enabled by the multi-view approach, including cross-view word discrimination and word similarity."}]