Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Today, a large amount of audio data is available on the b in the form of audiobooks, podcasts, video lectures, video blogs, news bulletins, etc. In addition,  can effortlessly record and store audio data such as a read, lecture, or impromptu speech on handheld devices. These data are rich in prosody and provide a plethora of voices to choose from, and their availability can significantly reduce the overhead of data preparation and help rapid building of synthetic voices. But, a few problems are associated with readily using this data such as (1) these audio files are generally long, and audio-transcription alignment is memory intensive; (2) precise corresponding transcriptions are unavailable, (3) many times, no transcriptions are available at all; (4) the audio may contain dis-fluencies and non-speech noises, since they are not specifically recorded for building synthetic voices; and (5) if  obtain automatic transcripts, they will not be error free. Earlier works on long audio alignment addressing the first and second issue generally preferred reasonable transcripts and mainly focused on (1) less manual intervention, (2) mispronunciation detection, and (3) segmentation error recovery. In this work,  use a large vocabulary public domain automatic speech recognition (ASR) system to obtain transcripts, follod by confidence measure-based data pruning which together address the five issues with the found data and also ensure the above three points. For proof of concept,  build voices in the English language using an audiobook (read speech) in a female voice from LibriVox and a lecture (spontaneous speech) in a male voice from Coursera, using both reference and hypotheses transcriptions, and evaluate them in terms of intelligibility and naturalness with the help of a perceptual listening test on the Blizzard 2013 corpus.
['Tejas Godambe', 'Sai Krishna Rallabandi', 'Suryakanth V. Gangashetty', 'Ashraf Alkhairy', 'Afshan Jafri']
I am writing a research paper on a idea Developing a unit selection voice given audio without corresponding text
[{"from": "human", "value": "I am writing a research paper on a idea Developing a unit selection voice given audio without corresponding text"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Today, a large amount of audio data is available on the b in the form of audiobooks, podcasts, video lectures, video blogs, news bulletins, etc. In addition,  can effortlessly record and store audio data such as a read, lecture, or impromptu speech on handheld devices. These data are rich in prosody and provide a plethora of voices to choose from, and their availability can significantly reduce the overhead of data preparation and help rapid building of synthetic voices. But, a few problems are associated with readily using this data such as (1) these audio files are generally long, and audio-transcription alignment is memory intensive; (2) precise corresponding transcriptions are unavailable, (3) many times, no transcriptions are available at all; (4) the audio may contain dis-fluencies and non-speech noises, since they are not specifically recorded for building synthetic voices; and (5) if  obtain automatic transcripts, they will not be error free. Earlier works on long audio alignment addressing the first and second issue generally preferred reasonable transcripts and mainly focused on (1) less manual intervention, (2) mispronunciation detection, and (3) segmentation error recovery. In this work,  use a large vocabulary public domain automatic speech recognition (ASR) system to obtain transcripts, follod by confidence measure-based data pruning which together address the five issues with the found data and also ensure the above three points. For proof of concept,  build voices in the English language using an audiobook (read speech) in a female voice from LibriVox and a lecture (spontaneous speech) in a male voice from Coursera, using both reference and hypotheses transcriptions, and evaluate them in terms of intelligibility and naturalness with the help of a perceptual listening test on the Blizzard 2013 corpus."}]