Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   present results from a study seeking to distinguish "unprepared" from "prepared" speech in broadcast news media. The idea is to explore the results from a previous experiment concerning the characterization of filled pauses and extensions, extending the analysis of such hesitation phenomena to large audio corpus. Daily news broadcasts of Portuguese television re segmented and labeled manually in terms of several speech styles, over a range of background environments. An automatic detection of filled pauses and extensions in this audio data allod us to correlate the presence of hesitation events with segments of unprepared speech. Distinguishing unprepared speech from prepared speech is of considerable practical interest for audio segmentation, speech processing and linguistic research. The long-term objective of this work is to automatically segment all audio genres and speaking styles as ll as identify prosodic and linguistic features of the speech segments.
['Arlindo Veiga', 'Sara Candeias', 'Dirce Celorico', 'Jorge Proença', 'Fernando Perdigão']
I am writing a research paper on a idea Towards automatic classification of speech styles
[{"from": "human", "value": "I am writing a research paper on a idea Towards automatic classification of speech styles"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   present results from a study seeking to distinguish \"unprepared\" from \"prepared\" speech in broadcast news media. The idea is to explore the results from a previous experiment concerning the characterization of filled pauses and extensions, extending the analysis of such hesitation phenomena to large audio corpus. Daily news broadcasts of Portuguese television re segmented and labeled manually in terms of several speech styles, over a range of background environments. An automatic detection of filled pauses and extensions in this audio data allod us to correlate the presence of hesitation events with segments of unprepared speech. Distinguishing unprepared speech from prepared speech is of considerable practical interest for audio segmentation, speech processing and linguistic research. The long-term objective of this work is to automatically segment all audio genres and speaking styles as ll as identify prosodic and linguistic features of the speech segments."}]