IBM inches toward human-like accuracy for speech recognition

Microsoft claimed to reach a 5.9 percent word error rate last October using neural language models resembling associative word clouds. At the time, the company believed 5.9 percent was equivalent to human parity. But, IBM says it's not popping the champagne yet. "As part of our process in reaching today's milestone, we determined human parity is actually lower than what anyone has yet achieved -- at 5.1 percent," George Saon, IBM principal research scientist, wrote in a blog post this week. IBM reached the 5.5 percent milestone by combining so-called Long Short-Term Memory, an artificial neural network, and WaveNet language models with three strong acoustic models.