Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: , the development of a closed captioning system for Filipino TV news programs is discussed. The researchers tested the system for offline captioning and evaluated the performance of the system based on word error rate (WER). Carnegie Mellon University's open-source speech recognition system, Sphinx-III, was used as the primary training and recognition engine. A Filipino News Corpus was built consisting of speech and text data obtained from Filipino news videos. Training and testing sets re generated and from this, different training and decoding parameters of Sphinx re evaluated. Using the word error rate (WER) computation, the highest average recognition accuracy achieved in developing for the test set was 57.36% using flat start context-dependent models and a language model with absolute discounting applied. This project is a first step towards establishing the baseline accuracy for future development of the system.
['Federico M. Ang', 'Maria Czarina Burgos', 'Marvin De Lara']
I am writing a research paper on a idea Automatic speech recognition for closed-captioning of Filipino news broadcasts
[{"from": "human", "value": "I am writing a research paper on a idea Automatic speech recognition for closed-captioning of Filipino news broadcasts"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: , the development of a closed captioning system for Filipino TV news programs is discussed. The researchers tested the system for offline captioning and evaluated the performance of the system based on word error rate (WER). Carnegie Mellon University's open-source speech recognition system, Sphinx-III, was used as the primary training and recognition engine. A Filipino News Corpus was built consisting of speech and text data obtained from Filipino news videos. Training and testing sets re generated and from this, different training and decoding parameters of Sphinx re evaluated. Using the word error rate (WER) computation, the highest average recognition accuracy achieved in developing for the test set was 57.36% using flat start context-dependent models and a language model with absolute discounting applied. This project is a first step towards establishing the baseline accuracy for future development of the system."}]