Clarification on riva ASR confidence score?

Hardware - GPU T4
Operating System - Ubuntu 20.04
Riva Version - 2.7.0

Hi, I try to infer on the pre-train Riva ASR model and the output is as follows

$ riva_asr_client --audio_file=/opt/riva/wav/en-US_sample.wav  --word_time_offsets=True
Word                                    Start (ms)      End (ms)        Confidence      

What                                    840             880             -1.5961e+00     
is                                      1160            1200            -6.1294e-01     
Natural                                 1800            2080            -2.5625e+00     
Language                                2200            2520            -5.9124e-01     
Processing?                             2720            3200            3.6569e-01 

My question is what does confidence represent? And what is the range of this confidence? I found the score it not 0 to 1 range


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Hi @mehadi.hasan

Thanks for your interest in Riva,

I have checked with the Riva team regarding the details on the requested ASR confidence score questions, will get back shortly once I have the answers


Hi @mehadi.hasan

I have the updates from the team,

Please find the link below

It depends on the Decoder used

Decoder Word Confidence Utterance Confidence
Greedy Minimum log probabilty accross the span of accoutic frames which represent the word, excluding blank tokens. Mean word confidence
OpenSeq2Seq (os2s) Scores are accumulated via a prefix beam search for CTC with an LM. Word scores are simply the accumulation from the frames accociated with that word. Scores are accumulated as above for the entire utterance.
Flashlight roughly a simple sum of log AM probabilities plus LM scores for the frames of a the word roughly a simple sum of log AM probabilties plus LM scores for the whole utterance
Kaldi log probabilty of the word given by the associated arc in the lattice. log probabilty of the utternace given by the associated path through the lattice.
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Hi, @rvinobha Thanks for the answer. It was really helpful

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