  Recently, there has been growth in providers of speech transcription services
enabling others to leverage technology they would not normally be able to use.
As a result, speech-enabled solutions have become commonplace. Their success
critically relies on the quality, accuracy, and reliability of the underlying
speech transcription systems. Those black box systems, however, offer limited
means for quality control as only word sequences are typically available. This
paper examines this limited resource scenario for confidence estimation, a
measure commonly used to assess transcription reliability. In particular, it
explores what other sources of word and sub-word level information available in
the transcription process could be used to improve confidence scores. To encode
all such information this paper extends lattice recurrent neural networks to
handle sub-words. Experimental results using the IARPA OpenKWS 2016 evaluation
system show that the use of additional information yields significant gains in
confidence estimation accuracy. The implementation for this model can be found
online.
