Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In many applications, like indexing of broadcast news or surveillance applications, the input data consists of a continuous, unsegmented audio stream. Speech recognition technology, hover, usually requires segments of relatively short length as input. For such applications, effective methods to segment continuous audio streams into homogeneous segments are required. , three different segmenting strategies (model-based, metric-based and energy-based) are compared on the same broadcast news test data. It is shown that model-based and metric-based techniques outperform the simpler energy-based algorithms. While model based segmenters achieve very high level of segment boundary precision, the metric-based segmenter preforms better in terms of segment boundary recall (RCL). To combine the advantages of both strategies, a new hybrid algorithm is introduced. For this, the results of a preliminary metric-based segmentation are used to construct the models for the final model-based segmenter run. The new hybrid approach is shown to outperform the other segmenting strategies.
['Thomas Kemp', 'Michael Schmidt', 'Martin Westphal', 'Alex Waibel']
I am writing a research paper on a idea Strategies for automatic segmentation of audio data
[{"from": "human", "value": "I am writing a research paper on a idea Strategies for automatic segmentation of audio data"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In many applications, like indexing of broadcast news or surveillance applications, the input data consists of a continuous, unsegmented audio stream. Speech recognition technology, hover, usually requires segments of relatively short length as input. For such applications, effective methods to segment continuous audio streams into homogeneous segments are required. , three different segmenting strategies (model-based, metric-based and energy-based) are compared on the same broadcast news test data. It is shown that model-based and metric-based techniques outperform the simpler energy-based algorithms. While model based segmenters achieve very high level of segment boundary precision, the metric-based segmenter preforms better in terms of segment boundary recall (RCL). To combine the advantages of both strategies, a new hybrid algorithm is introduced. For this, the results of a preliminary metric-based segmentation are used to construct the models for the final model-based segmenter run. The new hybrid approach is shown to outperform the other segmenting strategies."}]