Abstract: A system matches text-to-speech (TTS) or other output to a quality of an input spoken utterance. The system uses trained models to detect a speech quality and generates an indicator of the speech quality. The speech quality may be determined from audio or non-audio data. The indicator is sent to downstream components of the system such as a command processor or TTS system. The output of the system is then determined using the indicator of speech quality, thus customizing an output of the system to the manner in which the utterance was spoken.

Abstract: In an automatic speech recognition (ASR) processing system, ASR processing may be configured to reduce a latency of returning speech results to a user. The latency may be determined by comparing a time stamp of an utterance in process to a current time. Latency may also be estimated based on an endpoint of the utterance or other considerations such as how difficult the utterance may be to process. To improve latency the ASR system may be configured to adjust various processing parameters, such as graph pruning factors, path weights, ASR models, etc. Latency checks and corrections may occur dynamically for a particular utterance while it is being processed, thus allowing the ASR system to adjust to rapidly changing latency conditions.