Speech Recognition - Current Research and Funding

Current Research and Funding

Measuring progress in speech recognition performance is difficult and controversial. Some speech recognition tasks are much more difficult than others. Word error rates on some tasks are less than 1%. On others they can be as high as 50%. Sometimes it even appears that performance is going backward, as researchers undertake harder tasks that have higher error rates.

Because progress is slow and is difficult to measure, there is some perception that performance has plateaued and that funding has dried up or shifted priorities. Such perceptions are not new. In 1969, John Pierce wrote an open letter that did cause much funding to dry up for several years. In 1993 there was a strong feeling that performance had plateaued and there were workshops dedicated to the issue. However, in the 1990s, funding continued more or less uninterrupted and performance continued, slowly but steadily, to improve.

For the past thirty years, speech recognition research has been characterized by the steady accumulation of small incremental improvements. There has also been a trend to change focus to more difficult tasks due both to progress in speech recognition performance and to the availability of faster computers. In particular, this shifting to more difficult tasks has characterized DARPA funding of speech recognition since the 1980s. In the last decade, it has continued with the EARS project, which undertook recognition of Mandarin and Arabic in addition to English, and the GALE project, which focused solely on Mandarin and Arabic and required translation simultaneously with speech recognition.

Commercial research and other academic research also continue to focus on increasingly difficult problems. One key area is to improve robustness of speech recognition performance, not just robustness against noise but robustness against any condition that causes a major degradation in performance. Another key area of research is focused on an opportunity rather than a problem. This research attempts to take advantage of the fact that in many applications there is a large quantity of speech data available, up to millions of hours. It is too expensive to have humans transcribe such large quantities of speech, so the research focus is on developing new methods of machine learning that can effectively utilize large quantities of unlabeled data. Another area of research is better understanding of human capabilities and to use this understanding to improve machine recognition performance.