Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Conversational speech recognition has served as a flagship speech recognition task since the release of the Switchboard corpus in the 1990s. ,  measure the human error rate on the widely used NIST 2000 test set, and find that our latest automated system has reached human parity. The error rate of professional transcribers is 5.9% for the Switchboard portion of the data, in which newly acquainted pairs of people discuss an assigned topic, and 11.3% for the CallHome portion where friends and family members have open-ended conversations. In both cases, our automated system establishes a new state of the art, and edges past the human benchmark, achieving error rates of 5.8% and 11.0%, respectively. The key to our system's performance is the use of various convolutional and LSTM acoustic model architectures, combined with a novel spatial smoothing method and lattice-free MMI acoustic training, multiple recurrent neural network language modeling approaches, and a systematic use of system combination.
['Wayne Xiong', 'Jasha Droppo', 'Xuedong Huang', 'Frank Seide', 'Mike Seltzer', 'Andreas Stolcke', 'Dong Yu', 'Geoffrey Zweig']
I am writing a research paper on a idea Achieving Human Parity in Conversational Speech Recognition
[{"from": "human", "value": "I am writing a research paper on a idea Achieving Human Parity in Conversational Speech Recognition"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Conversational speech recognition has served as a flagship speech recognition task since the release of the Switchboard corpus in the 1990s. ,  measure the human error rate on the widely used NIST 2000 test set, and find that our latest automated system has reached human parity. The error rate of professional transcribers is 5.9% for the Switchboard portion of the data, in which newly acquainted pairs of people discuss an assigned topic, and 11.3% for the CallHome portion where friends and family members have open-ended conversations. In both cases, our automated system establishes a new state of the art, and edges past the human benchmark, achieving error rates of 5.8% and 11.0%, respectively. The key to our system's performance is the use of various convolutional and LSTM acoustic model architectures, combined with a novel spatial smoothing method and lattice-free MMI acoustic training, multiple recurrent neural network language modeling approaches, and a systematic use of system combination."}]