Microsoft's System Has a Word Error Rate Equal to That of Humans
References: blogs.microsoft & theverge
A team of engineers and researchers from Microsoft Artificial Intelligence and Research have designed a speech recognition system with a word error rate equitable to that of humans.
With more and more technologies and systems in place that listen to users' voices for commands, including Microsoft's own Cortana, speech recognition systems need to be incredibly accurate. The new system from Microsoft is so accurate that it has achieved parity with professional human transcriptionists. The system's word error rate, or the percentage of incorrect words in a transcription, is 5.9 percent, which is equal to the average for humans.
The researchers attribute the achievement to their use of neural networks, since these programs group words by meaning instead of sound. Thus, the word "fast" in the computer's brain is similar to "quick" but not to "feast,' for example.
With more and more technologies and systems in place that listen to users' voices for commands, including Microsoft's own Cortana, speech recognition systems need to be incredibly accurate. The new system from Microsoft is so accurate that it has achieved parity with professional human transcriptionists. The system's word error rate, or the percentage of incorrect words in a transcription, is 5.9 percent, which is equal to the average for humans.
The researchers attribute the achievement to their use of neural networks, since these programs group words by meaning instead of sound. Thus, the word "fast" in the computer's brain is similar to "quick" but not to "feast,' for example.
Trend Themes
1. Neural Network Speech Recognition - Opportunity to develop speech recognition systems that use neural networks to group words by meaning instead of sound.
2. Human-level Speech Recognition Accuracy - Opportunity to develop speech recognition systems with word error rates equal to that of humans.
3. Voice-activated Technology Accuracy - Opportunity to improve the accuracy of voice-activated technology through the use of more accurate speech recognition systems.
Industry Implications
1. Artificial Intelligence - Opportunity for AI companies to develop speech recognition systems that use neural networks to achieve human-level accuracy.
2. Virtual Assistant - Opportunity for virtual assistant companies to improve accuracy and provide a better user experience through the use of more accurate speech recognition systems.
3. Transcription Services - Opportunity for transcription companies to use speech recognition systems to improve speed and accuracy of their services.
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