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Current ASR systems are mainly trained and evaluated at the utterance level.Long range cross utterance context can be incorporated. A key task is to derivea suitable compact representation of the most relevant history contexts. Incontrast to previous researches based on either LSTM-RNN encoded histories thatattenuate the information from longer range contexts, or frame levelconcatenation of transformer context embeddings, in this paper compactlow-dimensional cross utterance contextual features are learned in theConformer-Transducer Encoder using specially designed attention pooling layersthat are applied over efficiently cached preceding utterances history vectors.Experiments on the 1000-hr Gigaspeech corpus demonstrate that the proposedcontextualized streaming Conformer-Transducers outperform the baseline usingutterance internal context only with statistically significant WER reductionsof 0.7% to 0.5% absolute (4.3% to 3.1% relative) on the dev and test data.

Towards Effective and Compact Contextual Representation for Conformer Transducer Speech Recognition Systems